update README

README.md

@@ -59,20 +59,20 @@
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 <div align="center">
-  <a href="https://skkuautolab.github.io/TSBOW/"><img src="https://img.shields.io/static/v1?label=TSBOW&message=Website&color=9a33fc" style="height: 25px;"></a>  
+  <a href="https://skkuautolab.github.io/TSBOW/"><img src="https://img.shields.io/static/v1?label=TSBOW&message=Website&color=9a33fc&logo=githubpages" style="height: 25px;"></a>  
   <a href="https://aaai.org/conference/aaai/aaai-26/"><img src="https://img.shields.io/static/v1?label=DOI_AAAI&message=updating&color=green" style="height: 25px;"></a>  
   <a href="https://aaai.org/conference/aaai/aaai-26/"><img src="https://img.shields.io/static/v1?label=Supplementary_arXiv&message=updating&color=FF0066&logo=arxiv" style="height: 25px;"></a>  
   <br>
-  <a href="https://docs.google.com/presentation/d/1Wd2alQk565YBZjTaoVdSrdDacb_ILhlXTOzTTP_tTt4/edit?usp=sharing"><img src="https://img.shields.io/static/v1?label=Slides&message=Presentation&color=fa9f1b&logo=googleslides" style="height: 25px;"></a>   
+  <!-- <a href="https://docs.google.com/presentation/d/1Wd2alQk565YBZjTaoVdSrdDacb_ILhlXTOzTTP_tTt4/edit?usp=sharing"><img src="https://img.shields.io/static/v1?label=Slides&message=Presentation&color=fa9f1b&logo=googleslides" style="height: 25px;"></a>    -->
   <a href="https://github.com/SKKUAutoLab/TSBOW"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=6699FF&logo=github" style="height: 25px;"></a>   
   <a href="https://huggingface.co/datasets/SKKUAutoLab/TSBOW"><img src="https://img.shields.io/static/v1?label=Dataset&message=HuggingFace&color=FF6600&logo=huggingface" style="height: 25px;"></a>  
 </div>
 
 <br>
 
-(UPDATING....) 
+**(UPDATING....)**
 
-(All links would be updated on the conference day.)
+(All links would be updated **on the conference day.**)
 
 Please download our Github repo to get better markdown view (i.e. Visual Code).
 
@@ -95,7 +95,8 @@ Please download our Github repo to get better markdown view (i.e. Visual Code).
 
 ## Abstract
 
-Global warming has intensified the frequency and severity of extreme weather events, which degrade CCTV signal and video quality while disrupting traffic flow, thereby increasing traffic accident rates. Existing datasets, often limited to light haze, rain, and snow, fail to capture extreme weather conditions. To address this gap, this study introduces the **T**raffic **S**urveillance **B**enchmark for **O**ccluded Vehicles under Various **W**eather Conditions (**TSBOW**), a comprehensive dataset designed to enhance occluded vehicle detection across diverse annual weather scenarios. Comprising over 32 hours of real-world traffic data from densely populated urban areas, TSBOW includes more than 48,000 manually annotated bounding boxes and 3.2 million semi-labeled boxes, spanning eight traffic participant classes from large vehicles to micromobility devices and pedestrians. We establish an object detection benchmark for TSBOW, highlighting challenges posed by occlusions and adverse weather. With its varied road types, scales, and viewpoints, TSBOW serves as a critical resource for advancing Intelligent Transportation Systems. Our findings underscore the potential of CCTV-based traffic monitoring, paving the way for new research and applications. The TSBOW dataset is available at: https://github.com/SKKUAutoLab/TSBOW.
+Global warming has intensified the frequency and severity of extreme weather events, which degrade CCTV signal and video quality while disrupting traffic flow, thereby increasing traffic accident rates. Existing datasets, often limited to light haze, rain, and snow, fail to capture extreme weather conditions. To address this gap, this study introduces the **T**raffic **S**urveillance **B**enchmark for **O**ccluded Vehicles under Various **W**eather Conditions (**TSBOW**), a comprehensive dataset designed to enhance occluded vehicle detection across diverse annual weather scenarios. Comprising over **32 hours** of real-world traffic data from densely populated urban areas, TSBOW includes more than **48,000 manually annotated** and **3.2 million semi-labeled** frames; bounding boxes spanning eight traffic participant classes from large vehicles to micromobility devices and pedestrians. We establish an object detection benchmark for TSBOW, highlighting challenges posed by occlusions and adverse weather. With its varied road types, scales, and viewpoints, TSBOW serves as a critical resource for advancing Intelligent Transportation Systems. Our findings underscore the potential of CCTV-based traffic monitoring, paving the way for new research and applications. The TSBOW dataset is publicly available at the following link. <br>
+**Code** -- https://github.com/SKKUAutoLab/TSBOW
 
 
 <!-- MARK: Overview -->
@@ -213,6 +214,32 @@ Global warming has intensified the frequency and severity of extreme weather eve
 
 
 
+<!-- MARK: Datasets -->
+
+## Datasets
+
+| Dataset           | Introduction                      | Year  |  Pub  | Paper |
+|:---:              |:---                               | :---: | :---: | :--- |
+| **UAVDT**         <br>[[website]](https://datasetninja.com/uavdt)| <span style="color: #FFCC00">Hardware</span>: UAVs. <br> <span style="color: #33CCCC">Tasks</span>: object detection, single object tracking, multiple-object tracking. <br> <span style="color: #FF6600">Position</span>: China. <br> <span style="color: #6699FF">Weather</span>: sunny/cloudy, fog, rain. <br> <span style="color: #FF0066">Time</span>: day, night. | 2020 | IJCV | The Unmanned Aerial Vehicle Benchmark: Object Detection, Tracking and Baseline |
+| **UA-DETRAC**     <br>[[website]](https://sites.google.com/view/daweidu/projects/ua-detrac?authuser=0)| <span style="color: #FFCC00">Hardware</span>: Cannon EOS 550D camera. <br> <span style="color: #33CCCC">Tasks</span>: object detection, multi-object tracking. <br> <span style="color: #FF6600">Position</span>: China. <br> <span style="color: #6699FF">Weather</span>: sunny/cloudy, rain. <br> <span style="color: #FF0066">Time</span>: day, night. | 2020 | CVIU | UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking |
+| **AAU RainSnow**  <br>[[website]](https://vbn.aau.dk/en/datasets/aau-rainsnow-traffic-surveillance-dataset/)| <span style="color: #FFCC00">Hardware</span>: RGB color and thermal camera. <br> <span style="color: #33CCCC">Tasks</span>: instance segmentation, single object tracking, multiple-object tracking. <br> <span style="color: #FF6600">Position</span>: Denmark. <br> <span style="color: #6699FF">Weather</span>: fog, rain, snow. <br> <span style="color: #FF0066">Time</span>: day, night. | 2019 | ITS | Rain Removal in Traffic Surveillance: Does it Matter? |
+| **<span style="color: #FFCC00">T</span><span style="color: #33CCCC">S</span><span style="color: #FF6600">B</span><span style="color: #6699FF">O</span><span style="color: #FF0066">W</span>**             <br>[[website]](https://skkuautolab.github.io/TSBOW/)| <span style="color: #FFCC00">Hardware</span>: CCTV system + color camera. <br> <span style="color: #33CCCC">Tasks</span>: object detection. <br> <span style="color: #FF6600">Position</span>: South Korea. <br> <span style="color: #6699FF">Weather</span>: sunny/cloudy, haze, rain, snow. <br> <span style="color: #FF0066">Time</span>: day. <br> (night-time and other tasks will be updated later) | 2026 | AAAI | TSBOW: Traffic Surveillance Benchmark for Occluded Vehicles Under Various Weather Conditions |
+
+
+
+<!-- MARK: Baselines -->
+
+## Baselines
+
+|  Year |  Pub    | Paper                            | Link  | Note |
+| :---: |  :---:  | :---                             |:---:  | :--- |
+| 2024  |  ICDICI | A review on yolov8 and its advancements | [paper](https://link.springer.com/chapter/10.1007/978-981-99-7962-2_39) | YOLOv8 |
+| 2024  |  arXiV  | YOLOv11: An Overview of the Key Architectural Enhancements | [paper](https://arxiv.org/abs/2410.17725) | YOLOv11 |
+| 2024  |  CVPR   | DETRs Beat YOLOs on Real-time Object Detection | [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_DETRs_Beat_YOLOs_on_Real-time_Object_Detection_CVPR_2024_paper.html) | RT-DETR |
+| 2025  |  arXiV  | A Breakdown of the Key Architectural Features | [paper](https://arxiv.org/abs/2502.14740) | YOLOv12 |
+
+
+
 <!-- MARK: Experiments -->
 
 ## Experiments
@@ -288,6 +315,12 @@ Global warming has intensified the frequency and severity of extreme weather eve
 
 Thanks to the developers and contributors of the following open-source repositories, whose invaluable work has greatly inspire our project:
 
+Datasets:
+- [UAVDT](https://datasetninja.com/uavdt): A traffic dataset contains drone footages under sunny and rainy conditions.
+- [UA-DETRAC](https://sites.google.com/view/daweidu/projects/ua-detrac?authuser=0): A traffic surveillance dataset captures sunny and rainy weather.
+- [AAU RainSnow](https://vbn.aau.dk/en/datasets/aau-rainsnow-traffic-surveillance-dataset/): A traffic surveillance dataset provides segmentation annotations for rain and snow weather.
+
+Github Repo:
 - [X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling): An open-source tool for precise bounding box creation.
 - [Ultralytics YOLO](https://github.com/ultralytics/ultralytics): Detection models for training and real-time inferencing.
 - [YOLOv12](https://github.com/sunsmarterjie/yolov12): A model for object detection.