Abnormal Human Behavior Detection Improvement with an Efficient Attention Block
Anh Dung Ho, Huong-Giang Doan, Ngoc‐Trung Nguyen · Engineering Technology & Applied Science Research · 2025
Convolution Neural Networks (CNNs) have become an attractive method for the detection of anomalous behaviors. However, designing an efficient CNN model in terms of classification accuracy remains a challenging problem. Furthermore, the existing datasets for abnormal behavior detection are limited, with each focusing on a certain context. Therefore, a CNN model trained on a certain dataset will be adaptive for a particular context and not suitable for other contexts. This study proposes a CNN framework with an efficient attention mechanism to capture key information from multiple inputs, namely RGB, optical flow, and heatmap. Experiments were carried out on several benchmark datasets and a self-collected dataset, and the evaluation involved both single- and cross-dataset strategies. The results show the superior performance of the proposed frameworks compared to other SOTA methods in detection accuracy.