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.

翻译：全球变暖加剧了极端天气事件的频率和严重程度，这些事件会降低闭路电视信号和视频质量，同时扰乱交通流，从而提高交通事故率。现有数据集通常仅限于轻度雾霾、雨和雪，未能捕捉极端天气条件。为弥补这一不足，本研究引入了多种天气条件下遮挡车辆的交通监控基准（TSBOW），这是一个旨在提升跨多样年度天气场景下遮挡车辆检测能力的综合性数据集。TSBOW包含来自人口密集城市区域超过32小时的真实交通数据，涵盖超过48,000帧人工标注和320万帧半标注帧；边界框覆盖从大型车辆到微型交通工具及行人等八类交通参与者。我们为TSBOW建立了目标检测基准，突显了遮挡和恶劣天气带来的挑战。凭借其多样的道路类型、尺度和视角，TSBOW可作为推进智能交通系统的关键资源。我们的研究结果强调了基于闭路电视的交通监控潜力，为新的研究和应用铺平了道路。TSBOW数据集公开于：https://github.com/SKKUAutoLab/TSBOW。