Abnormal Human Behavior Detection Improvement with an Efficient Attention Block

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.