| | --- |
| | license: cc-by-4.0 |
| | tags: |
| | - LiDAR, |
| | - Point cloud |
| | - Point cloud augmentation |
| | - Object detection |
| | - People detection |
| | size_categories: |
| | - 100K<n<1M |
| | --- |
| | # Dataset Card for PointCloudPeople |
| |
|
| | The use of light detection and ranging (LiDAR) sensor technology for people detection offers a significant advantage in terms of data protection. However, to design |
| | these systems cost- and energy-efficiently, the relationship between the measurement data and final object detection output with deep neural networks (DNNs) has to be |
| | elaborated. Therefore, we present an automatically labeled LiDAR dataset for person detection, with different disturbance objects and various augmentation methods to |
| | analyze the influence of the LiDAR sensor parameters distance, resolution, noise, and shading in real point clouds. |
| |
|
| | <h3 align="center"> |
| | <a><img src="docs/images/Mensa_000b.jpg" width="500"></a> |
| | </h3> |
| | |
| | ## Dataset Details |
| |
|
| | The data set is structured as follows. As described in the corresponding paper, an original data set consisting of point clouds in .pcd format was recorded with a |
| | Blickfeld Qb2 LiDAR sensor and automatically labeled in .json format using GDPR-compliant cameras (Basic). This dataset was then augmented with different sensor and |
| | mounting parameters (distance, noise, resolution, shading). |
| |
|
| | - **Curated by:** Lukas Haas |
| | - **License:** CC-BY-4.0 |
| |
|
| | ### Dataset Sources |
| |
|
| | The data set consists of approx—2000 point clouds with the corresponding labels per sub-data set collected with a Blickfeld Qb2 LiDAR Sensor. The augmentation generated a |
| | total of 95 derivatives of the dataset. The data set contains at least one person per point cloud and other interfering objects in the sensor's FOV. The subjects in the |
| | FoV are of different sizes. |
| |
|
| | ### Dataset Structure |
| |
|
| | Based on the measurement setup presented in the paper a dataset is recorded with the Qb2 to train the DNNs. Based on this dataset, different datasets with various |
| | resolutions, distances to the scene, noise, and shading of people in the point cloud of the LiDAR sensor are augmented to investigate the individual influence of the |
| | parameters on the object detection performance of DNNs. The point clouds are in .pcd files with the x, y, z, intensit format, and the |
| | labels in .json files in the unified 3D box definition label format for each object. |
| |
|
| | ### Personal and Sensitive Information |
| |
|
| | Due to the anonymity of people in LiDAR point clouds, personal data is protected. Furthermore, GDPR conform cameras are used to label the point clouds automatically. |
| |
|
| | ## Bias, Risks, and Limitations |
| |
|
| | Considering the limitations of our dataset, real-world tests should be conducted. GDPR conforms with care in a safe environment. |
| | However, it should be noted that although the data set includes people of different body sizes, the majority of people are adults due to the location of the measurement |
| | setup. |
| |
|
| | ## Citation |
| |
|
| | <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
| |
|
| | **BibTeX:** |
| |
|
| | @Article{s25103114, |
| | AUTHOR = {Haas, Lukas and Sanne, Florian and Zedelmeier, Johann and Das, Subir and Zeh, Thomas and Kuba, Matthias and Bindges, Florian and Jakobi, Martin and Koch, Alexander W.}, |
| | TITLE = {LiDAR Sensor Parameter Augmentation and Data-Driven Influence Analysis on Deep-Learning-Based People Detection}, |
| | JOURNAL = {Sensors}, |
| | VOLUME = {25}, |
| | YEAR = {2025}, |
| | NUMBER = {10}, |
| | ARTICLE-NUMBER = {3114}, |
| | URL = {https://www.mdpi.com/1424-8220/25/10/3114}, |
| | PubMedID = {40431906}, |
| | ISSN = {1424-8220}, |
| | DOI = {10.3390/s25103114} |
| | } |
| |
|
| | **APA:** |
| |
|
| | Haas, L., Sanne, F., Zedelmeier, J., Das, S., Zeh, T., Kuba, M., Bindges, F., Jakobi, M., & Koch, A. W. (2025). LiDAR Sensor Parameter Augmentation and Data-Driven Influence Analysis on Deep-Learning-Based People Detection. Sensors, 25(10), 3114. https://doi.org/10.3390/s25103114 |
| |
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