A Machine Learning Approach for Tracking and Predicting Abnormal Behaviour in Python

Abstract:

Abnormal behavior detection refers to the problem of finding patterns in data that do not conform to expected behavior. Detection of abnormal behavior is an important area of research in computer vision and is also driven by a wide of application domains, such as smart video surveillance. In this paper, we present a novel based-energy approach for abnormal behavior detection. Use an adaptive optical flow model to operate on moving particles instead of objects and fuses features with the shape and trajectory information. we introduce an integrated multiple behavior model for accurate abnormal behavior detection in a complex crowd scene. We use not only the personal behavior model, but also multiple social behavior models. The experimental results show that our proposed method efficiently detects the abnormal behavior in a crowded scene. To detect the abnormal behavior, experimental results on the Institute of Automation, Chinese Academy of Science multi-view behavior database and self-photo videos demonstrate the robustness and effectiveness of our method.