TSBOW: Traffic Surveillance Benchmark for Occluded Vehicles Under Various Weather Conditions
It provides a critical resource for advancing Intelligent Transportation Systems by tackling occlusions and adverse weather in traffic monitoring, though it is incremental as it focuses on dataset creation rather than novel methods.
This study introduced the TSBOW dataset to address the lack of extreme weather data for occluded vehicle detection in traffic surveillance, comprising over 32 hours of real-world traffic data with more than 48,000 manually annotated frames and 3.2 million semi-labeled frames across eight traffic participant classes.
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.