TY  - JOUR
T1  - Lightweight Temporal Segment Network for Video Scene Understanding: Validation in Driver Assault Detection
AU  - Lee, Juneyong 
AU  - Kim, Joon 
AU  - Park, Junhui 
AU  - Jo, Jongho 
AU  - Jang, Ikbeom 
JO  - Journal of KIISE, JOK
PY  - 2024
DA  - 2024/1/14
DO  - 10.5626/JOK.2024.51.11.987
KW  - temporal segment network
KW  - video scene understanding
KW  - Anomaly Detection
KW  - driverassault
AB  - "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."