Enhancing Intelligent Video Surveillance: Deep Learning Approaches for Human Anomalous Behavior Recognition

Modeling anomalous behavior pattern has become as a significant research domain in the recent years due to the security demands in public places. In literature some of the existing approaches such as statistical-based, density-based are applied for pattern detection whereas traditional approaches may not suitable for all scenarios, since they are limited with their properties. In this study, we propose deep learning-based behavior recognition to characterize the abnormality through train and test the frame. Proposed system used the AlexNet Convolution Neural Networks (CNN) architecture, which has largely trained dataset used for feature extraction. In addition, optical flow applied to estimate the human motion. Moreover, motion influence map utilized to reflects the characteristics of the human motion. The CNN-based proposed system can steadily outperforms abnormal behavior detection, especially for the lightweight models when given a small among of training samples.