@article{ZHANG2026, 
author = {Dongping ZHANG and Xin PAN and Daobin MA and Hongmei MI and Lili LIN},
title = {Spatial-temporal enhanced abnormal behavior detection based on multi-channel coupling},
year = {2026},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {52},
number = {1},
pages = {73-79},
keywords = {SlowFast, spatio-temporal enhanced, abnormal behavior detection, multi-channel coupling, attention mechanism},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2023.0752},
doi = {10.13700/j.bh.1001-5965.2023.0752},
abstract = {To address the limitations of existing abnormal behavior detection models—particularly their inadequate feature representation and insufficient modeling of dynamic temporal characteristics—this paper proposes a multi-channel coupled spatio-temporal enhanced anomaly detection method. Built upon the SlowFast network architecture, the proposed approach integrates a multi-channel coupled spatial enhancement module into the slow pathway to strengthen static feature modeling, and a multi-channel coupled temporal enhancement module into the fast pathway to improve the discriminability of dynamic temporal features. Extensive experiments on three benchmark datasets—Violent Flow, Hockey Fight, and Real-life Violence Situations—demonstrate that the proposed method achieves prediction accuracies of 95.3%, 97.3%, and 94%, respectively, outperforming current state-of-the-art approaches. The results validate the superior feature representation capability and generalization performance of the proposed method in abnormal behavior recognition tasks.}
}