TSBOW -- Traffic Surveillance Benchmark for Occluded Vehicles Under Various Weather Conditions

TL;DR

TSBOW is a comprehensive, publicly available dataset designed to improve occluded vehicle detection in traffic surveillance under various weather conditions, addressing limitations of existing datasets.

Contribution

This paper introduces TSBOW, a large-scale dataset with diverse weather scenarios and annotations, advancing research in traffic monitoring and occluded vehicle detection.

Findings

Established a new object detection benchmark using TSBOW.

Highlighted challenges of occlusions and adverse weather in traffic monitoring.

Demonstrated the dataset's potential to enhance intelligent transportation systems.

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 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.