Year: 2025
Type: article
Abstract: This paper proposes a lightweight violent behavior recognition model, DualCascadeTSF-MobileNetV2, which is improved based on the temporal shift module and its subsequent research. By introducing the Dual Cascade Temporal Shift and Fusion module, the model further enhances the feature correlation ability in the time dimension and solves the problem of information sparsity caused by multiple temporal shifts. Meanwhile, the model incorporates the efficient lightweight structure of MobileNetV2, significantly reducing the number of parameters and computational complexity. Experiments were conducted on three public violent behavior datasets, Crowd Violence, RWF-2000, and Hockey Fights, to verify the performance of the model. The results show that it outperforms other classic models in terms of accuracy, computational speed, and memory size, especially among lightweight models. This research continues and expands on the previous achievements in the fields of TSM and lightweight network design, providing a new solution for real-time violent behavior recognition on edge devices.
Source: Applied Sciences
DOI: 10.3390/app15073862
Authors: Yuang Chen, Yong Li, Shaohua Li, Shuhan Lv, Fang Lin
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4409059714