Video-based Examination Anomaly Action Recognition via Channel-Temporal Model

With rapid technological advancements in computer vision, the recognition of abnormal behavior during examinations has transitioned from human observation to computer-assisted recognition. Although traditional 2D Convolutional Neural Networks (CNNs) excel in computational efficiency, they need to capture crucial temporal dynamics for comprehensive video analysis more precisely. Nevertheless, 3D CNN-based methods demonstrate promising performance in temporal modeling but impose substantial computational demands and deployment costs. To overcome these challenges, this paper introduces an innovative Examination Anomaly Action Recognition Network named ReTANet. It incorporates cross-channel temporal modeling to capture temporal features within videos. It also employs Multi-Scale Channel Attention to enrich feature representation and extract channel and spatial information, thereby enhancing recognition accuracy without significantly increasing computational complexity and model parameters. Furthermore, this paper introduces the Examination Anomaly Action Dataset, also named the ExamGuard Dataset (EGD), to facilitate model training and evaluation. Remarkably, our model demonstrates superior performance compared to existing mainstream action recognition algorithms on the HMDB-5l dataset. Rigorous ablation studies conducted on the UCF-101 dataset have shown the effectiveness and significance of the proposed module.