构建极端天气下遮挡车辆检测数据集，助力智能交通系统提升安全性
TSBOW -- Traffic Surveillance Benchmark for Occluded Vehicles Under Various Weather Conditions
- 构建涵盖多种极端天气的实拍交通数据集，含超48000个标注帧
- 包含32小时真实城市道路视频，覆盖8类交通参与者及多视角
- 适合研究遮挡检测、恶劣天气视觉感知与智能交通系统优化者
全球变暖加剧了极端天气事件的频率与严重性，导致监控信号退化、视频质量下降并扰乱交通流，进而提高事故率。现有数据集多限于轻雾、雨、雪等温和天气，难以覆盖极端条件。为此，本文提出面向多种天气条件下遮挡车辆的交通监控基准数据集TSBOW，涵盖超过32小时密集城市区域的真实交通数据，包含48,000多个人工标注帧与320万条半自动标注帧，覆盖从大型车辆到微型交通工具及行人的八类交通参与者，提供边界框标注。我们建立了基于TSBOW的目标检测基准，揭示遮挡与恶劣天气带来的挑战。其多样化的道路类型、尺度与视角使其成为推进智能交通系统的重要资源。研究结果凸显了基于CCTV的交通监控潜力，为新研究与应用铺平道路。数据集已公开：https://github.com/SKKUAutoLab/TSBOW。
原文摘要 · Abstract (English)
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 Traffic Surveillance Benchmark for Occluded vehicles under various Weather 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, pave the way for new research and applications. The TSBOW dataset is publicly available at: https://github.com/SKKUAutoLab/TSBOW.
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