Lightweight Temporal Segment Network for Video Scene Understanding: Validation in Driver Assault Detection

Juneyong Lee, Joon Kim, Junhui Park, Jongho Jo, Ikbeom Jang

http://doi.org/10.5626/JOK.2024.51.11.987

"The number of driver assaults in transportation such as taxis and buses has been increasing over the past few years. It can be especially difficult to respond quickly to assaults on drivers by drunks late at night. To address this issue, our research team proposed a lightweight CNN-based Temporal Segment Network (TSN) model that could detect driver assaults by passengers in real time. The TSN model efficiently processes videos by sampling a small number of image frames and divides videos into two streams for learning: one for spatial information processing and the other for temporal information processing. Convolutional neural networks are employed in each stream. In this research, we applied a lightweight CNN architecture, MobileOne, significantly reducing the model size while demonstrating improved accuracy even with limited computing resources. The model is expected to contribute to rapid response and prevention of hazardous situations for drivers when it is integrated into vehicular driver monitoring systems."