climecc111/FastTracker-Benchmark

--- license: bigscience-openrail-m task_categories: - object-detection language: - en tags: - Multi-object-tracking pretty_name: FastTracker-Benchmark size_categories: - 100K<n<1M --- # FastTracker Benchmark ### A new benchmark dataset comprising diverse vehicle classes with frame-level tracking annotation introduced in paper: *FastTracker: Real-Time and Accurate Visual Tracking* _[Hamidreza Hashempoor](https://hamidreza-hashempoor.github.io/), Yu Dong Hwang_. ## Resources | Github | Paper | |:-----------------:|:-------:| |[](https://github.com/Hamidreza-Hashempoor/FastTracker)|[](https://arxiv.org/abs/2508.14370) <div align="center"> <img src="./fig/fasttrack_benchmark.jpg" width="40%" alt="MiroThinker" /> </div> --- ## Dataset Overview Brief statistics and visualization of FastTracker benchmark and its comparison with other benchmarks. | Attribute | UrbanTracker | CityFlow | FastTracker | |----------------|--------------|----------|-----------| | **Year** | 2014 | 2022 | 2025 | | **Detections** | 12.5K | 890K | 800K | | **#Videos** | 5 | 40 | 12 | | **Obj/Frame** | 5.4 | 8.2 | 43.5 | | **#Classes** | 3 | 1 | 9 | | **#Scenarios** | 1 | 4 | 12 | --- ## Dataset Summary - **What is it?** FastTrack is a large-scale benchmark dataset for evaluating multi-object tracking in complex and high-density traffic environments. It includes 800K annotated object detections across 12 videos, with an average of 43.5 objects per frame. The dataset features 9 traffic-related classes and covers diverse real-world traffic scenarios—such as multilane intersections, tunnels, crosswalks, and merging roads—captured under varying lighting conditions (daytime, nighttime, shadows). - **Why was it created?** FastTrack was created to address limitations of existing benchmarks like UrbanTracker and CityFlow, which lack diversity in scene types and have lower object density. This benchmark introduces challenging conditions including extreme crowding, long-term occlusions, and diverse motion patterns, to push the boundaries of modern multi-object tracking algorithms—particularly those optimized for real-world, urban traffic settings. - **What can it be used for?** Multi-object tracking, re-identification, online tracking evaluation, urban scene understanding, and benchmarking tracking algorithms under occlusion and crowding. - **Who are the intended users?** Researchers and practitioners in computer vision and intelligent transportation systems, especially those focusing on real-time tracking, urban mobility, autonomous driving, and edge deployment. Also valuable for students and developers working on lightweight or environment-aware tracking models. --- ## Dataset Structure ### Data Format GT format is like (each line): `frame, id, bb_left, bb_top, bb_width, bb_height, conf, class, 1.0`. To prepare the dataset, first run `extract_frames.py` to decode frames from each video. In **line 11** of the script, add the video filename and the number of frames you want to extract. ```bash python extract_frames.py ``` Then, convert the ground truth into COCO format with: ```bash python convert_to_coco.py ``` This will generate annotations/train.json ready for training your detector. ## Citation If you use our code or Benchmark, please cite our work. ``` @misc{hashempoor2025fasttrackerrealtimeaccuratevisual, title={FastTracker: Real-Time and Accurate Visual Tracking}, author={Hamidreza Hashempoor and Yu Dong Hwang}, year={2025}, eprint={2508.14370}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2508.14370}, } ```许可证：bigscience-openrail-m 任务类别：目标检测 语言：英语 标签：多目标跟踪(Multi-object-tracking) 友好名称：FastTracker基准数据集 规模类别：10万 < n < 100万 --- # FastTracker基准数据集 ### 本文《FastTracker：实时精准视觉跟踪》中提出的包含多样车辆类别与帧级跟踪标注的新型基准数据集 作者：Hamidreza Hashempoor、Yu Dong Hwang。 ## 资源 | GitHub | 论文 | |:-----------------:|:-------:| |[](https://github.com/Hamidreza-Hashempoor/FastTracker)|[](https://arxiv.org/abs/2508.14370)| <div align="center"> <img src="./fig/fasttrack_benchmark.jpg" width="40%" alt="MiroThinker" /> </div> --- ## 数据集概览 FastTracker基准数据集的简要统计信息、可视化效果，以及与其他基准数据集的对比。 | 属性 | UrbanTracker | CityFlow | FastTracker | |----------------|--------------|----------|-----------| | **发布年份** | 2014 | 2022 | 2025 | | **总检测框数** | 12.5K | 890K | 800K | | **视频总数** | 5 | 40 | 12 | | **平均每帧目标数** | 5.4 | 8.2 | 43.5 | | **目标类别数** | 3 | 1 | 9 | | **场景总数** | 1 | 4 | 12 | --- ## 数据集摘要 - **是什么？** FastTracker是用于在复杂高密度交通环境中评估多目标跟踪的大规模基准数据集。该数据集包含12段视频中的80万个标注检测框，平均每帧包含43.5个目标。数据集涵盖9类交通相关目标，包含多样化的真实交通场景——如多车道交叉口、隧道、人行横道与合流道路——并采集了不同光照条件下的画面（日间、夜间、阴影环境）。 - **为何创建该数据集？** 现有基准数据集如UrbanTracker与CityFlow存在场景类型单一、目标密度较低的局限，FastTracker正是为弥补这些不足而开发。本基准数据集引入了极端拥挤、长期遮挡与多样运动模式等挑战性条件，以推动现代多目标跟踪算法的性能边界，尤其是针对真实城市交通场景优化的算法。 - **可用于哪些场景？** 多目标跟踪、重识别、在线跟踪评估、城市场景理解，以及在遮挡与拥挤场景下的跟踪算法基准测试。 - **目标用户群体是谁？** 计算机视觉与智能交通系统领域的研究人员与从业者，尤其是聚焦实时跟踪、城市交通、自动驾驶与边缘部署的研究者。同时，该数据集对研究轻量级或环境感知跟踪模型的学生与开发者也具有较高价值。 --- ## 数据集结构 ### 数据格式 真实框（Ground Truth，GT）格式如下（每行一条数据）： `frame, id, bb_left, bb_top, bb_width, bb_height, conf, class, 1.0`。 如需准备数据集，请首先运行`extract_frames.py`从每段视频中提取帧。 在该脚本的第11行，添加待提取的视频文件名与所需提取的帧数。 bash python extract_frames.py 随后使用以下脚本将真实框转换为COCO（Common Objects in Context）格式： bash python convert_to_coco.py 该脚本将生成用于训练检测器的`annotations/train.json`标注文件。 --- ## 引用说明 若您使用本代码或基准数据集，请引用我们的工作： @misc{hashempoor2025fasttrackerrealtimeaccuratevisual, title={FastTracker: Real-Time and Accurate Visual Tracking}, author={Hamidreza Hashempoor and Yu Dong Hwang}, year={2025}, eprint={2508.14370}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2508.14370}, }