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text stringlengths 1 129k | id stringlengths 9 16 | article_id stringlengths 6 10 | section_title stringlengths 1 1.26k | educational_score float64 0.46 5.16 | domain stringclasses 3
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About 42 (14.38 %) had 15 mg/kg to 80 mg/kg, 9 (3.08 %) had >80 mg/kg, 188 (64.4 %) had 1.1 mg/kg to 14.9 mg/kg, and 53 (18.2 %) had no iodine in the salt (0 mg/kg). Only 26 (8.9 %) of the households had used iodized salt properly. | PMC467049_p22 | PMC467049 | Results and discussion | 2.332361 | biomedical | Study | [
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Generally, commercial salts have higher iodine levels compared to local salts, which agrees with the present study. The amount of iodine in salt from the studied villages was influenced by regulatory measures, processing methods , and the natural variability of source mineral deposits present, clearly observed in comme... | PMC467049_p23 | PMC467049 | Results and discussion | 4.070018 | biomedical | Study | [
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Results in Table 3 indicate that most samples meet the regulatory standards set by TBS and WHO for nitrate and sulphate content, with the exception of phosphate. The concentration range was nitrate (3.30–4.40 mg/kg, 5.45–7.40 mg/kg), sulphate (0.31–0.42 mg/kg, 0.03–0.07 mg/kg) for local and commercially branded salt, r... | PMC467049_p24 | PMC467049 | Results and discussion | 4.166809 | biomedical | Study | [
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According to Table 4 , the ammonia content was consistent across all samples, with values ranging from 0.5 mg/kg to 0.6 mg/kg falling within the range recommended (1.0 mg/kg) and found to be not significant statistically (p > 0.001). Copper concentrations ranged from 0.9 mg/kg to 2.0 mg/kg across the samples, with samp... | PMC467049_p25 | PMC467049 | Results and discussion | 4.159823 | biomedical | Study | [
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In a study by Ref. on metal contaminants Tehran in edible salts, Pb (1.59 ± 0.90), Cd (0.91 ± 0.32), Zn (6.02 ± 2.54), Fe (17.8 ± 6.11), Cu (1.24 ± 0.90), and Al (5.82 ± 0.61) mg/Kg in salts were Pb (0.86 ± 0.52), Cd (0.65 ± 0.34), Zn (6.5 ± 4.86), Fe (15.3 ± 5.95), Cu (1.21 ± 0.79), and Al (5.60 ± 0.75) mg/Kg in table... | PMC467049_p26 | PMC467049 | Results and discussion | 4.177166 | biomedical | Study | [
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The socioeconomic characteristics of households in Nkonkilangi, Mangwanjuki, Unyanga, Chibumagwa, and Kinyambwa villages are presented in Table 5 , categorized as education level, income, and occupation. Table 5 Household socioeconomic characteristics. Table 5 Socioeconomic status Category Villages Nkonkilangi Mangwanj... | PMC467049_p27 | PMC467049 | Results and discussion | 3.111598 | biomedical | Study | [
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The highest percentage of households with informal education was found in Chibumagwa 24 (22.6 %), followed by Kinyambwa 23 (45.1 %). Nkonkilangi village has the highest percentage of households with primary and secondary education, at 74 (45.4 %) and 51 (31.3 %), respectively. | PMC467049_p28 | PMC467049 | Results and discussion | 1.421987 | other | Other | [
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Tertiary education is least prevalent across all villages, with the highest percentage of 29 (30.2 %) in Mangwanjuki village. As for income levels, Nkonkilangi has the highest percentage of households with low income at 88 (54.0 %), followed by Chibumagwa at 33 (31.1 %). This suggests a significant proportion of househ... | PMC467049_p29 | PMC467049 | Results and discussion | 2.64523 | other | Study | [
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It is indicated in Table 5 that peasants dominated in all villages, forming the majority of households with the highest percentages. While price, quality, accessibility, and convenience are important factors for all groups, the specific priorities and considerations of peasants, businessmen, and employees led them to m... | PMC467049_p30 | PMC467049 | Results and discussion | 2.273582 | other | Study | [
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Table 6 shows household consumption of commercial-branded and local salt in different sources and villages, in which out of 470 households surveyed, 234 (49.88 %) consumed local and 236 (50.12 %) consumed commercial-branded salt, with varying prevalence of the types of salts across villages that were significantly diff... | PMC467049_p31 | PMC467049 | Results and discussion | 3.968595 | biomedical | Study | [
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The study revealed a significant difference in the composition of minerals in commercially branded versus locally sourced salts from Bahi, Iramba, Manyoni, and Singida urban districts in Tanzania. Local salts often had higher concentrations of essential minerals compared to branded salts, yet iodine content was inconsi... | PMC467049_p32 | PMC467049 | Conclusions | 4.115186 | biomedical | Study | [
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The data will be made available upon request. | PMC467049_p33 | PMC467049 | Data availability statement | 0.769031 | other | Other | [
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Questionnaire survey for households in the studied villages. | PMC467049_p34 | PMC467049 | Additional information | 1.525435 | biomedical | Other | [
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Jackson Henry Katonge: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. | PMC467049_p35 | PMC467049 | CRediT authorship contribution statement | 1.008929 | other | Other | [
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The author declares that have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. | PMC467049_p36 | PMC467049 | Declaration of competing interest | 0.982726 | other | Other | [
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With the acceleration of people's lifestyles and the rise of emerging industries, such as logistics and food delivery, electric bicycles and their convenience, environmental friendliness, and energy efficiency, have become the preferred mode of urban transportation. However, in complex traffic environments, the failure... | PMC467071_p0 | PMC467071 | Introduction | 1.897356 | other | Other | [
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In recent years, object tracking algorithms in computer vision technology have made significant advancements and yielded fruitful results. In 2016, Bertinetto et al. introduced SiamFC, which is a single-object tracking algorithm based on Siamese networks. It employs an end-to-end fully convolutional network for estimat... | PMC467071_p1 | PMC467071 | Introduction | 2.644715 | other | Review | [
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In this regard, Bewley et al. introduced an important multi-object tracking algorithm in 2016, namely the SORT algorithm. The algorithm utilizes Faster R–CNN as the object detector, employs Kalman filters for object trajectory prediction, and utilizes the Hungarian algorithm to find the specific matching object boundin... | PMC467071_p2 | PMC467071 | Introduction | 3.25731 | other | Study | [
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These improvements enable the DeepSORT algorithm to better handle object association challenges, especially in cases of prolonged occlusion, resulting in a significant enhancement in tracking performance. However, the SORT and DeepSORT algorithms often rely on bounding boxes with high confidence scores above specific t... | PMC467071_p3 | PMC467071 | Introduction | 2.24771 | other | Other | [
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Due to the exceptional performance of the Transformer architecture across various domains, researchers have been prompted to introduce it for multi-object tracking . Accordingly, in 2022, Zhou X et al. proposed an innovative global multi-object tracking algorithm based on the Transformer architecture. This approach too... | PMC467071_p4 | PMC467071 | Introduction | 1.458394 | other | Other | [
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Subsequently, in 2023, Zhang Y et al. , building upon MOTR , introduced MOTRv2. They incorporated an additional object detector to provide prior detection information to MOTR, consequently enhancing end-to-end multi-object tracking performance. However, it should be noted that tracking algorithms based on the Transform... | PMC467071_p5 | PMC467071 | Introduction | 1.35796 | other | Other | [
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Currently, in research related to applications in traffic monitoring scenarios, Zihan P et al. conducted a study on the recognition of wrong-way riding behavior of electric bicycles. They employed a hybrid Gaussian model to extract background and used background subtraction to extract the foreground of electric bicycle... | PMC467071_p6 | PMC467071 | Introduction | 1.570218 | other | Study | [
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Xiaoping W et al. , in response to challenges related to occlusion, rotation, and scale transformation, have enhanced the MDnet algorithm to improve object tracking accuracy in complex traffic scenarios. They employed optical flow change information and small convolutional kernels to track and predict motor vehicles, n... | PMC467071_p7 | PMC467071 | Introduction | 1.359434 | other | Study | [
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Zhenxiao L et al. proposed a multi-vehicle tracking algorithm to address real-time performance and ID switching issues in multi-object tracking. They first employed YOLOv3 as the object detector and then, in combination with the DeepSORT tracking algorithm, introduced an LSTM motion model for tracking vehicle objects i... | PMC467071_p8 | PMC467071 | Introduction | 1.405233 | other | Other | [
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Caihong L et al. have designed a cross-view multi-object tracking visualization algorithm based on field-of-view stitching. They utilized the geometric information of video scenes to achieve field-of-view stitching, presenting tracked objects from different perspectives in a unified field of view. This algorithm facili... | PMC467071_p9 | PMC467071 | Introduction | 1.427235 | other | Other | [
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The following table is a comprehensive overview of the literature review, outlining the strengths and weaknesses associated with each method. | PMC467071_p10 | PMC467071 | Introduction | 2.674774 | biomedical | Review | [
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Precisely, the existing algorithms have not effectively addressed different issues, such as frequent ID switching and fragmented tracking trajectories in complex traffic scenarios. Electric bicycle riders may pass through the monitoring area from a distance or up close, leading to variable object scales. Furthermore, d... | PMC467071_p11 | PMC467071 | Introduction | 1.159822 | other | Other | [
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Despite the progress made in object tracking algorithms, there are still several gaps that require attention. Firstly, the current algorithms have not adequately addressed the challenge of frequent ID switching in complex traffic scenarios, resulting in fragmented tracking trajectories. This problem is especially preva... | PMC467071_p12 | PMC467071 | Introduction | 1.000967 | other | Other | [
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Therefore, in this paper, an electric bicycle tracking algorithm has been proposed, EBTrack, designed for traffic monitoring scenarios. | PMC467071_p13 | PMC467071 | Introduction | 0.996279 | other | Other | [
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The EBTrack algorithm utilizes cutting-edge distance measurement technologies, as outlined by Liu and Bao , that make use of ultra-wideband sensors to enable accurate real-time monitoring of electric bicycles in city traffic. Additionally, our strategy builds upon the fundamental research on distance measurement techno... | PMC467071_p14 | PMC467071 | Introduction | 1.866426 | other | Other | [
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The EBTrack tracking algorithm offers several advantages. Firstly, by utilizing the lightweight YOLOv7 object detector, efficient and accurate object detection has been achieved, providing reliable object position information, thereby enhancing tracking algorithm accuracy and stability. Secondly, the ResNetEB feature e... | PMC467071_p15 | PMC467071 | Introduction | 2.751775 | other | Other | [
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The proposed method presents a number of enhancements compared to the standard YOLOv7 model for electric bicycle tracking. The improvements include the introduction of the ResNetEB Feature Extraction Network, which is specifically designed for electric bicycle re-identification. This network enhances performance in sce... | PMC467071_p16 | PMC467071 | Introduction | 2.612352 | other | Study | [
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The EBTrack tracking algorithm has established a more robust data foundation, thereby facilitating effective violation recognition in traffic monitoring scenarios. | PMC467071_p17 | PMC467071 | Introduction | 1.135092 | other | Other | [
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YOLOv7 combines high accuracy with lightweight design, performing excellently across a range from 5FPS to 160FPS, making it a powerful solution for various application scenarios. Compared to previous detectors, such as YOLOX, SSD , and Faster R–CNN, YOLOv7 has achieved significant improvements in both accuracy and spee... | PMC467071_p18 | PMC467071 | The object detector YOLOv7 | 1.531159 | other | Other | [
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Additionally, YOLOv7 introduces a novel ELAN (Efficient Layer Aggregation Networks) structure, which maintains model performance while reducing computational complexity. As shown in Fig. 1 , where k represents convolution's kernel size, and s represents stride. YOLOv7 also introduces a training method with auxiliary he... | PMC467071_p19 | PMC467071 | The object detector YOLOv7 | 1.607971 | other | Other | [
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The ByteTrack tracking algorithm is based on object detection. Similar to other non-Re-ID algorithms, it solely uses the bounding boxes obtained after object detection for tracking. The algorithm employs Kalman filtering to predict bounding boxes and then uses the Hungarian algorithm for matching between objects and tr... | PMC467071_p20 | PMC467071 | The ByteTrack tracking algorithm | 1.494593 | other | Other | [
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The primary idea behind the ByteTrack tracking algorithm is to create tracking trajectories and use these trajectories to match objects in each frame, forming complete trajectories frame by frame. While the ByteTrack tracking algorithm demonstrates commendable performance in object tracking, there are several aspects t... | PMC467071_p21 | PMC467071 | The ByteTrack tracking algorithm | 1.884732 | other | Other | [
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The EBTrack algorithm has been specifically developed to effectively monitor electric bicycles in intricate traffic situations. It incorporates the YOLOv7 object detector, the NSA Kalman filter, and the ResNetEB feature extraction network, in addition to a customized matching mechanism. The comprehensive structure of t... | PMC467071_p22 | PMC467071 | Method | 2.296759 | other | Study | [
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To address the issues present in the ByteTrack tracking algorithm, the proposed EBTrack tracking algorithm starts by utilizing a high-performance object detector, YOLOv7, as the foundation for electric bicycle tracking. YOLOv7 is known for its high precision and lightweight design, enabling accurate detection of electr... | PMC467071_p23 | PMC467071 | The EBTrack tracking algorithm | 1.309881 | other | Other | [
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To ensure real-time performance, feature extraction is only performed when generating new object IDs, preventing frequent feature extraction from negatively impacting the tracking algorithm's real-time capabilities. Additionally, for more accurate trajectory predictions as well as enhanced tracking precision and stabil... | PMC467071_p24 | PMC467071 | The EBTrack tracking algorithm | 1.620828 | other | Other | [
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The EBTrack tracking algorithm is designed through the fusion of the YOLOv7 object detector, NSA Kalman filter, and the object re-identification network ResNetEB, along with a matching mechanism. This design enables the algorithm to demonstrate excellent performance in terms of accuracy and real-time tracking, particul... | PMC467071_p25 | PMC467071 | EBTrack tracking algorithm basic workflow | 2.56927 | other | Other | [
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The algorithm utilizes YOLOv7 as the object detector, performing object detection in each frame of the video. Firstly, the detection results are categorized into two classes based on the bounding box confidence, including high confidence and low confidence ( Table 2 , from line 3 to line 13). Secondly, the NSA Kalman f... | PMC467071_p26 | PMC467071 | EBTrack tracking algorithm basic workflow | 2.230165 | other | Other | [
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0.000865375273860991
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During the process of tracking electric bicycles, there are instances where objects are either occluded or not correctly detected by the object detector. This can result in objects briefly disappearing and then reappearing in the detector's field of view. Relying solely on the detector's results in such situations can ... | PMC467071_p27 | PMC467071 | The re-identification network ResNetEB | 1.21433 | other | Other | [
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] | [
0.030396495014429092,
0.9681719541549683,
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0.0007016632007434964
] | en | 0.999998 |
By analyzing the original DeepSORT re-identification network, it was observed that it consists of 6 residual blocks, ultimately outputting features with a dimension of 128. However, this network structure is relatively simple, making it challenging to accurately capture real-time changes in the appearance of electric b... | PMC467071_p28 | PMC467071 | The re-identification network ResNetEB | 1.252032 | other | Other | [
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0.0004977824282832444,
0.971030592918396
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0.08047740906476974,
0.9170977473258972,
0.0014991596108302474,
0.0009256380144506693
] | en | 0.999998 |
To address the aforementioned issue, two key improvement measures were implemented. Firstly, the feature dimension increased from 128 to 512 to enhance feature granularity and classification accuracy, thus strengthening the discrimination capability of the tracking algorithm. This enhancement allows the network to more... | PMC467071_p29 | PMC467071 | The re-identification network ResNetEB | 1.861963 | other | Study | [
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0.2979471981525421,
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] | en | 0.999997 |
The structure of a standard residual block in ResNetEB is illustrated in Fig. 3 (a) which comprises a main branch and a residual branch. In the figure, k represents the convolution's kernel size, s denotes the stride, and c indicates the number of channels. The main branch consists of two convolutional modules. It star... | PMC467071_p30 | PMC467071 | The re-identification network ResNetEB | 3.594221 | other | Study | [
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] | [
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] | en | 0.999998 |
The use of a residual structure is beneficial for maintaining the integrity of input information, reducing information loss in the forward propagation process, which is common in traditional convolutional layers. Furthermore, the network only needs to learn the differential parts between the input and output, simplifyi... | PMC467071_p31 | PMC467071 | The re-identification network ResNetEB | 3.364686 | other | Other | [
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] | [
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0.7771115899085999,
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] | en | 0.999996 |
Therefore, by increasing the feature dimension to 512 in the re-identification network of the original DeepSORT, the algorithm has enhanced feature granularity and classification accuracy. Additionally, by increasing the network's depth and designing the ResNetEB network, it can better capture changes in the appearance... | PMC467071_p32 | PMC467071 | The re-identification network ResNetEB | 1.340103 | other | Other | [
0.03280223533511162,
0.0006780146504752338,
0.9665197134017944
] | [
0.03489699587225914,
0.9636155962944031,
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0.0006419898709282279
] | en | 0.999997 |
In object tracking algorithms, the motion prediction module is a crucial component. Currently, tracking algorithms typically model the object's motion using a Kalman filter . However, linear Kalman filters have a limitation in that they use the same measurement noise scale for all objects, regardless of the quality of ... | PMC467071_p33 | PMC467071 | Kalman filter with adaptive modulation noise scale | 3.637068 | other | Study | [
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] | [
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In complex traffic surveillance scenarios, electric bicycles typically enter the monitoring frame from a distant or near locations rather than suddenly appearing at the center of the frame. This prior knowledge serves as a crucial basis for the current research. To better meet application requirements, a specialized ma... | PMC467071_p34 | PMC467071 | Introducing a specialized matching mechanism with prior knowledge | 1.050554 | other | Other | [
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] | [
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] | en | 0.999997 |
While ensuring that the electric bicycle detector meets the requirements for detection of accuracy and speed, the appearance of a new object ID in the monitoring frame typically indicates that the tracking algorithm may have lost a previously tracked object. In such cases, the following approach has been employed. | PMC467071_p35 | PMC467071 | Introducing a specialized matching mechanism with prior knowledge | 1.118175 | other | Other | [
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0.0009475096594542265,
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] | [
0.0055540781468153,
0.99358069896698,
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0.00042818894144147635
] | en | 0.999999 |
Firstly, when a new object is about to appear at the center of the monitoring frame, the feature re-identification network has been used to extract the new object's feature information and match it with the feature information of recently existing objects. This process is crucial as it aids in more accurately identifyi... | PMC467071_p36 | PMC467071 | Introducing a specialized matching mechanism with prior knowledge | 1.287488 | other | Other | [
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] | [
0.005613596644252539,
0.9936109185218811,
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0.0003257794014643878
] | en | 0.999998 |
Secondly, during the object matching process, several challenges are often encountered, including the presence of factors like occlusion, leading to temporary losses of objects during the tracking. Therefore, when a new object is about to appear in the central region of the monitoring frame, a delayed matching strategy... | PMC467071_p37 | PMC467071 | Introducing a specialized matching mechanism with prior knowledge | 1.57434 | other | Other | [
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] | [
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0.0005230872193351388
] | en | 0.999996 |
Furthermore, to maintain the real-time performance of the tracking algorithm, the appearance features are extracted only when a new object is initially generated and during the delayed matching period for the soon-to-be generated object. This strategy maximizes the utilization of the feature re-identification network w... | PMC467071_p38 | PMC467071 | Introducing a specialized matching mechanism with prior knowledge | 1.410718 | other | Other | [
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] | [
0.017001209780573845,
0.9820488095283508,
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] | en | 0.999997 |
The EBTrack tracking algorithm presents numerous benefits, such as the utilization of YOLOv7 for efficient and precise object detection, the enhancement of trajectory prediction through the NSA Kalman filter, and the reduction of ID switching via the specialized matching mechanism. In complex scenarios, the ResNetEB fe... | PMC467071_p39 | PMC467071 | Introducing a specialized matching mechanism with prior knowledge | 1.467295 | other | Other | [
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0.0007296651601791382,
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] | [
0.018162716180086136,
0.9805077910423279,
0.0008782473742030561,
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] | en | 0.999999 |
For algorithm training and validation, the hardware and software platform environment used consists of the following specifications: GPU: NVIDIA GeForce RTX 3060 Laptop GPU; CPU: 11th Gen Intel Core i5-11260H @ 2.60 GHz Hexa-core; VRAM: 6 GB; RAM: 32 GB; Operating System: Windows 10; Deep Learning Framework: PyTorch 1.... | PMC467071_p40 | PMC467071 | Experimental results and analysis | 1.717995 | other | Other | [
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] | [
0.02342892996966839,
0.9757351875305176,
0.00044926724513061345,
0.00038666074397042394
] | en | 0.999998 |
Currently, there is no existing dataset for electric bicycle detection and tracking. To meet the requirements of training and validation of electric bicycle tracking algorithms, data have been collected and annotated from existing traffic intersection cameras under various conditions, including different locations, tim... | PMC467071_p41 | PMC467071 | Dataset | 1.294565 | other | Study | [
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0.0005320552736520767,
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] | [
0.9402474164962769,
0.05812534689903259,
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] | en | 0.999996 |
In this study, the following evaluation metrics were used to assess algorithm performance, namely MOTA (Multiple Object Tracking Accuracy), IDF1 (ID F1-Score), FPS (Frames Per Second), Precision, and Recall. | PMC467071_p42 | PMC467071 | Evaluation metrics | 2.131239 | other | Study | [
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] | [
0.9720762968063354,
0.026643164455890656,
0.0008457436924800277,
0.0004349203663878143
] | en | 0.999997 |
MOTA (Multiple Object Tracking Accuracy) is one of the most widely used evaluation metrics in for object tracking, aimed at providing a comprehensive assessment of the performance of object tracking algorithms. MOTA takes into account three primary sources of tracking errors: FP (False Positives), FN (False Negatives),... | PMC467071_p43 | PMC467071 | MOTA (Multiple Object Tracking Accuracy) | 2.85272 | other | Other | [
0.08861066401004791,
0.0005409151781350374,
0.9108483195304871
] | [
0.3790643811225891,
0.6136648058891296,
0.006518148351460695,
0.0007525837863795459
] | en | 0.999996 |
The IDF 1 metric places more emphasis on association. It is a comprehensive evaluation metric for object tracking that focuses on both IDP (ID Precision) and IDR (ID Recall). It particularly emphasizes on the continuity of tracking and the accuracy of identity information. It measures the extent to which the tracking a... | PMC467071_p44 | PMC467071 | IDF 1(Identification F1) | 3.043911 | other | Other | [
0.11493269354104996,
0.0007032843423075974,
0.8843640089035034
] | [
0.29307880997657776,
0.701302170753479,
0.004993609618395567,
0.0006254615145735443
] | en | 0.999997 |
FPS is used to measure the real-time performance and efficiency of object tracking algorithms. In academic research and practical applications, FPS is typically used to assess the speed of object tracking algorithms when processing real-time video streams. | PMC467071_p45 | PMC467071 | FPS（Frames per second） | 1.319964 | other | Other | [
0.047798220068216324,
0.0009753096383064985,
0.9512264132499695
] | [
0.0044395350851118565,
0.9943006038665771,
0.0009533671545796096,
0.0003064630145672709
] | en | 0.999998 |
Precision refers to the ratio of the number of correctly detected objects by the detector to the total number of detections. It signifies the accuracy of the detector, indicating how many of the detection results are correct, as shown in Equation . (4) P r e c i s i o n = T P T P + F P here, TP (True Positives) represe... | PMC467071_p46 | PMC467071 | Precision | 3.167797 | biomedical | Other | [
0.6656105518341064,
0.0009878459386527538,
0.33340156078338623
] | [
0.24610480666160583,
0.75139319896698,
0.002082768129184842,
0.00041916937334463
] | en | 1 |
Recall refers to the ratio of the number of correctly detected objects by the detector to the total number of true objects. It indicates how many of the true objects the detector is able to find, as shown in Equation . (5) R e c a l l = T P T P + F N | PMC467071_p47 | PMC467071 | Recall | 1.517012 | other | Other | [
0.1467178612947464,
0.0014652329264208674,
0.8518169522285461
] | [
0.05339789390563965,
0.9445372223854065,
0.0012829047627747059,
0.0007819760940037668
] | en | 0.999998 |
Such that, FN (False Negatives) represents the number of targets that were not detected by the detector. | PMC467071_p48 | PMC467071 | Recall | 2.754801 | biomedical | Study | [
0.9853798747062683,
0.000690440705511719,
0.013929593376815319
] | [
0.49961793422698975,
0.4979294240474701,
0.0015816140221431851,
0.0008710096590220928
] | en | 0.999997 |
To validate the performance and characteristics of the proposed EBTrack tracking algorithm, an analysis is conducted from several perspectives. Firstly, a comparative evaluation of different object detectors is performed to determine the detector that performs optimally in electric bicycle tracking. Secondly, discussio... | PMC467071_p49 | PMC467071 | Experimental results and analysis | 1.66802 | other | Study | [
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0.0006781240808777511,
0.9632211327552795
] | [
0.9410654902458191,
0.05657259374856949,
0.0015525026246905327,
0.000809345452580601
] | en | 0.999996 |
In object tracking, the performance and accuracy of tracking algorithms are influenced by the quality of the object detector and the size of the dataset. In this study, a self-constructed dataset called Dataset-Det was used to train the object detector. During the training process, different detectors were iterated for... | PMC467071_p50 | PMC467071 | Comparison of different object detectors | 3.441724 | other | Study | [
0.33086198568344116,
0.0008715151925571263,
0.6682665348052979
] | [
0.9935703277587891,
0.005741512402892113,
0.0005381236551329494,
0.00015002561849541962
] | en | 0.999996 |
Balancing accuracy and real-time performance is a key concern. In this study, improvements were made to the existing DeepSORT re-identification network branch, and iterations of 200 times were performed on different feature dimensions using the Dataset-ID dataset. Subsequently, these enhancements were integrated into t... | PMC467071_p51 | PMC467071 | Comparison of Re-identification networks with different feature dimensions | 3.884767 | biomedical | Study | [
0.5995044112205505,
0.0010666640009731054,
0.39942899346351624
] | [
0.9985087513923645,
0.0010454814182594419,
0.00037132741999812424,
0.00007441412162734196
] | en | 0.999995 |
To validate the effectiveness of the major modules in the EBTrack tracking algorithm, the performance of different modules has been compared on validation set of the Dataset-Track. As shown in Table 6 , after introducing the YOLOv7 object detector, a significant improvement was observed in MOTA and IDF1, and a decreasi... | PMC467071_p52 | PMC467071 | Module ablation experiments | 3.618265 | biomedical | Study | [
0.5328369736671448,
0.0009556087316013873,
0.46620747447013855
] | [
0.9969285130500793,
0.002643631072714925,
0.0003196877078153193,
0.00010817128350026906
] | en | 0.999997 |
In this study, comparative experiments were conducted with the SORT, DeepSORT, FairMOT, and ByteTrack algorithms on validation set of the Dataset-Track, and the experimental results are presented in Table 7 . The SORT tracking algorithm exhibits frequent ID switches, which often prevent the continuous tracking of compl... | PMC467071_p53 | PMC467071 | Comparison of different tracking algorithms | 3.679947 | other | Study | [
0.4204539656639099,
0.0012712341267615557,
0.5782747268676758
] | [
0.9979792237281799,
0.00145382818300277,
0.0004643380525521934,
0.00010259317059535533
] | en | 0.999997 |
Table 7 represents the results of the EBTrack tracking algorithm on validation set of the Dataset-Track. It's worth noting that the issue of ID switches is relatively prominent in densely crowded and nighttime environments. | PMC467071_p54 | PMC467071 | Comparison of different tracking algorithms | 1.202919 | other | Other | [
0.040140409022569656,
0.0006172987050376832,
0.9592423439025879
] | [
0.2022148072719574,
0.7945472598075867,
0.0019349146168679,
0.001303026219829917
] | en | 0.999998 |
In the following, here are the extended experimental results. - Comparison with Additional Tracking Algorithms: | PMC467071_p55 | PMC467071 | Comparison of different tracking algorithms | 1.554418 | biomedical | Other | [
0.6285833716392517,
0.002734244568273425,
0.3686824142932892
] | [
0.4069480895996094,
0.585529625415802,
0.005497677717357874,
0.0020246400963515043
] | en | 0.999999 |
Tracktor : This algorithm utilizes a tracking-by-detection approach and incorporates a novel tracking-based branch to predict the bounding box location in the next frame. On the Dataset-Track validation set, Tracktor achieved an MOTA of 75.6 % and an IDF1 of 78.3 %. | PMC467071_p56 | PMC467071 | Comparison of different tracking algorithms | 1.280592 | other | Other | [
0.02497422695159912,
0.0007420760812237859,
0.9742836952209473
] | [
0.051462605595588684,
0.946492612361908,
0.0011054158676415682,
0.0009393333457410336
] | en | 0.999997 |
JDE : This is a joint detection and embedding method that combines detection and re-identification into a single network. JDE obtained an MOTA of 78.9 % and an IDF1 of 82.4 % on the Dataset-Track validation set. | PMC467071_p57 | PMC467071 | Comparison of different tracking algorithms | 1.653289 | other | Other | [
0.2046235352754593,
0.0010789919178932905,
0.794297456741333
] | [
0.10092858970165253,
0.8966426253318787,
0.0015595940640196204,
0.0008692152914591134
] | en | 0.999995 |
TransTrack : This algorithm employs a Transformer-based architecture for multi-object tracking. TransTrack achieved an MOTA of 81.2 % and an IDF1 of 84.6 % on the Dataset-Track validation set. - Comparison with Different Feature Dimensions: | PMC467071_p58 | PMC467071 | Comparison of different tracking algorithms | 1.333338 | other | Other | [
0.059882137924432755,
0.0008716742740944028,
0.9392462372779846
] | [
0.08636848628520966,
0.910901665687561,
0.0016490640118718147,
0.0010807339567691088
] | en | 0.999997 |
By decreasing the feature dimension to 256, the EBTrack algorithm was able to achieve an MOTA of 85.1 % and an IDF1 of 89.0 %. This reduction in feature dimensionality led to a minor decline in tracking accuracy but enhanced real-time performance . | PMC467071_p59 | PMC467071 | Comparison of different tracking algorithms | 2.092212 | other | Study | [
0.4969852864742279,
0.001735151745378971,
0.5012795329093933
] | [
0.795592188835144,
0.20193926990032196,
0.0013700993731617928,
0.0010983935790136456
] | en | 0.999996 |
On the other hand, by increasing the feature dimension to 1024, the EBTrack algorithm achieved an MOTA of 90.4 % and an IDF1 of 95.1 %. Although this higher feature dimension improved tracking accuracy, it also resulted in higher computational complexity. - Comparison with Different Object Detectors: | PMC467071_p60 | PMC467071 | Comparison of different tracking algorithms | 1.803794 | other | Study | [
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0.0014184946194291115,
0.7225066423416138
] | [
0.500745415687561,
0.49545201659202576,
0.0022493842989206314,
0.0015532433753833175
] | en | 0.999999 |
YOLOv5 : Using YOLOv5 as the object detector, the EBTrack algorithm achieved an MOTA of 78.1 % and an IDF1 of 81.3 % on the Dataset-Track validation set. | PMC467071_p61 | PMC467071 | Comparison of different tracking algorithms | 1.687275 | other | Other | [
0.17043426632881165,
0.0011634292313829064,
0.8284022808074951
] | [
0.2995965778827667,
0.6975849270820618,
0.001596168614923954,
0.0012223167577758431
] | en | 0.999998 |
Faster R–CNN : With Faster R–CNN as the object detector, EBTrack obtained an MOTA of 67.4 % and an IDF1 of 70.3 %, demonstrating the impact of detector performance on tracking accuracy. - Ablation Study: | PMC467071_p62 | PMC467071 | Comparison of different tracking algorithms | 1.97113 | biomedical | Study | [
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0.0013632309855893254,
0.35975441336631775
] | [
0.8299380540847778,
0.16803991794586182,
0.0011965460143983364,
0.0008254993008449674
] | en | 0.999996 |
EBTrack without NSA Kalman Filter: The removal of the NSA Kalman filter resulted in EBTrack achieving an MOTA of 84.4 % and an IDF1 of 88.6 %. The absence of the NSA Kalman filter led to slightly lower accuracy in trajectory predictions. | PMC467071_p63 | PMC467071 | Comparison of different tracking algorithms | 2.259219 | biomedical | Study | [
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0.0012593354331329465,
0.29596322774887085
] | [
0.8098811507225037,
0.18809252977371216,
0.001243394915945828,
0.0007829547394067049
] | en | 0.999996 |
EBTrack without ResNetEB: Excluding the ResNetEB feature extraction network led to EBTrack achieving an MOTA of 84.6 % and an IDF1 of 89.0 %. The absence of the ResNetEB network impacted the algorithm's performance in handling complex scenarios involving occlusions and appearance changes. | PMC467071_p64 | PMC467071 | Comparison of different tracking algorithms | 1.671591 | other | Other | [
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] | [
0.28122103214263916,
0.7155680060386658,
0.0017540563130751252,
0.0014568407787010074
] | en | 0.999994 |
These comparative analyses highlight the robustness and efficiency of the EBTrack tracking algorithm in electric bicycle tracking applications. | PMC467071_p65 | PMC467071 | Comparison of different tracking algorithms | 1.437195 | other | Other | [
0.05987745150923729,
0.000887296861037612,
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] | [
0.3148331940174103,
0.678462564945221,
0.00509493425488472,
0.0016093595186248422
] | en | 0.999998 |
In this study, an effective algorithm was introduced for tracking electric bicycles, named EBTrack, which is specifically tailored for traffic monitoring situations. The algorithm used the lightweight YOLOv7 as the object detector, ensuring precise and dependable object detection. The incorporation of the ResNetEB feat... | PMC467071_p66 | PMC467071 | Conclusion | 3.362666 | other | Study | [
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0.0009195468737743795,
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] | [
0.9945026636123657,
0.0041864169761538506,
0.0011196095729246736,
0.00019132389570586383
] | en | 0.999998 |
This research was funded by Natural Science Research Project of Anhui Province ，Natural Science Research Project of 10.13039/501100012404 Fuyang Normal University | PMC467071_p67 | PMC467071 | Funding | 0.993294 | other | Other | [
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] | [
0.0015212633879855275,
0.9975581169128418,
0.0005257150041870773,
0.0003948990779463202
] | en | 0.999996 |
All data generated or analysed during this study are included in this published article. | PMC467071_p68 | PMC467071 | Data availability | 0.785522 | biomedical | Other | [
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0.04161475971341133,
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] | en | 0.999998 |
Zhengyan Liu: Investigation. Chaoyue Dai: Investigation. Xu Li: Investigation. | PMC467071_p69 | PMC467071 | CRediT authorship contribution statement | 0.880195 | other | Other | [
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0.005479262210428715,
0.9932492971420288,
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. | PMC467071_p70 | PMC467071 | Declaration of competing interest | 0.981821 | other | Other | [
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Funding for this research was supported by NIH grant RO1-HL62150 to AHL. In addition, this work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences and Biosciences Division, under award #DE-SC0015662. | 39022598_p0 | 39022598 | Funding information | 0.980187 | other | Other | [
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The authors declare they have no known competing financial interests or personal relationships that may affect this work. | 39022598_p1 | 39022598 | Declaration of competing interest | 0.999675 | other | Other | [
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Theodore J. Kottom: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. Eva M. Carmona: Writing – review & editing, Writing – original draft. Bernd Lepenies: Resources. Andrew H. Limper: Writing – review & editing, Writing – original draft, Supervision, ... | 39022598_p2 | 39022598 | CRediT authorship contribution statement | 0.951842 | other | Other | [
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The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Andrew H Limper reports financial support was provided by National Institutes of Health. Theodore J Kottom reports financial support was provided by National Institutes of Health. Eva C... | 39022598_p3 | 39022598 | Declaration of competing interest | 0.998679 | other | Other | [
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Microphthalmia is defined as a small, underdeveloped eye caused by disrupted eye development through genetic or environmental factors in the first trimester. Clinical phenotypic heterogeneity exists in patients with varying severity and associated ocular and systemic features. As one of the most severe developmental ey... | 39007834_p0 | 39007834 | Introduction | 3.878931 | biomedical | Review | [
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Besides a small proportion of cases that are attributed to environmental factors, such as intrauterine infections and toxins, genetic alterations are the major causes of such a disease. 3 Genes implicated in main non-syndromic microphthalmia include SOX2 , OTX2 , RAX , VSX2 , STRA6 , RARB , ALDH1A3 , MAB21L2 , VAX1 , B... | 39007834_p1 | 39007834 | Introduction | 4.278555 | biomedical | Study | [
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Various studies suggest that Hedgehog ( HH ) signaling plays essential roles in human and mouse eye development. 5 Mutations/deletions in human Sonic Hedgehog ( SHH ) cause holoprosencephaly, including anophthalmia, cyclopia, and coloboma in severe cases. 6 – 8 Homozygous Shh null mutant mice show that Shh plays a crit... | 39007834_p2 | 39007834 | Introduction | 4.534348 | biomedical | Study | [
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Transcriptional factors GLI1, GLI2, and GLI3 are thought to regulate most of the transcriptional responses to HH signaling. 13 Different from GLI1, which acts predominantly as positive regulators of target genes in HH signaling, GLI2 and GLI3 play either an activating or a repressing role depending on the HH signal ava... | 39007834_p3 | 39007834 | Introduction | 4.664035 | biomedical | Study | [
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It is still an open question how GLI3 regulates eye formation, it would be of great importance to investigate the dosage-dependent function of GLI3 in eye development. Our group have long been interested in the lens development and lens-related diseases, in this context, we generated a lens-specific TgGli3Ki/Ki mouse l... | 39007834_p4 | 39007834 | Introduction | 4.16224 | biomedical | Study | [
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The first mouse microphthalmia transcription factor ( Mitf ) mutation was discovered over 60 years ago, which was originated from a cohort of irradiated mice. 32 Since then, most mouse models identified with a microphthalmia phenotype were created by forward genetics. For example, in a spontaneous mouse mutant line Pit... | 39007834_p5 | 39007834 | Introduction | 4.400205 | biomedical | Study | [
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The animal studies were conducted in accordance with the ARVO Animal Statement. We used C57BL/6JGpt mice as the background line. We made TgGli3Ki/Ki knockin mice via CRISPR/Cas9 system. The mouse Gli3 gene was inserted into Hipp 11 (H11) locus via Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISP... | 39007834_p6 | 39007834 | Generation of the TgGli3Ki/Ki Mice | 3.88637 | biomedical | Study | [
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Total RNA was extracted using TRIzol and measured on NanoDrop 2000 (Thermo Fisher Scientific). Reverse transcription was performed using an RT kit , and quantitative polymerase chain reaction (qPCR) was performed using SYBR Premix . The qPCR was performed on the ABI 7500 PCR machine, and data were analyzed using the AB... | 39007834_p7 | 39007834 | Quantitative Polymerase Chain Reaction | 4.088737 | biomedical | Study | [
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Protein extracts from eye tissues were subjected to SDS-PAGE and blotted onto 0.45 µm PVDF membrane (Merck Millipore), incubated overnight with the primary antibody GLI3 at 1 in 200 dilution, or β-Actin, at 1 in 20,000 dilution in Tris-buffered saline with 0.1% Tween-20 and 5% milk. The anti-goat second antibody was us... | 39007834_p8 | 39007834 | Western Blot | 4.073973 | biomedical | Study | [
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For hematoxylin and eosin (H&E) staining of mice tissues, dissected eyeballs were fixed in 10% formalin overnight, followed by dehydration through an ethanol gradient. Tissues were embedded in paraffin and sectioned at 5 µm. To observe the morphology of the entire eyeball, the tissue sections were stained with H&E. To ... | 39007834_p9 | 39007834 | Histology and Immunofluorescence | 4.070139 | biomedical | Study | [
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