Towards Transfer Learning Based Human Anomaly Detection in Videos

In the present era, the crime rates increase day by day, in such situations, humans keep security as a topmost priority in their daily lives. As a result, the demands of surveillance system surge for public, private, and remote areas. With that scenario, anomaly detection systems are gaining more attention in the domain of computer vision. Various machine (ML) and deep learning (DL) based approaches have been presented for anomaly detection over decades but still, this framework is a challenging task because of many reasons, one of them being the vague quality of content in the video. Transfer learning (TL) plays a key role by providing already trained information to gain good accuracy. This paper is divided into three parts: the first part comprises the study of deep and machine for violent and abnormal activities detection. In the second part, a basic architecture of transfer learning-based framework for anomaly detection along with TL approaches is presented. The final section compares machine learning and deep learning algorithms for the publicly available benchmark datasets based on accuracy achieved. The main obstacles encountered while utilizing this technique are also mentioned in accordance with study and analysis.