Implementation of an Anomalous Human Activity Recognition System

Abstract

This paper brings to light one of the most prominent applications of human activity recognition which is the anomaly detection. Providing security to an individual is a major concern of any society today due to the constantly increasing actions causing threats, starting from deliberate violence to an injury caused by an accident. Mere installation of a traditional closed-circuit television is not enough as it requires a human being to constantly remain alert and monitor the cameras, which is quite inefficient. This calls for the need to develop an automated security system that identifies abnormal activities in real time and brings immediate help to the victims. But it is time-consuming to recognize activities from lengthy surveillance videos and hence a new implementation idea which adaptively compresses the videos before passing it through the activity recognition system has been proposed. Adaptive video compression is a unique compression technology that compresses only the least significant parts of the video, retaining the objects of interest. By combining adaptive video compression and the convolutional 3D network with contextual multiple scales based on the temporal features (Xu et al. in Learning deep representations of appearance and motion for anomalous event detection. In: Computer Vision and Pattern Recognition (CVPR), 2015), the paper aims to provide an accurate anomalous human activity recognition system that works in real time. The method is implemented on the UCF101 crime dataset with around 13 different anomalies (Learning Spatiotemporal Features with 3D Convolutional Networks. In: International Conference on Computer Vision (ICCV), IEEE, 2015). It resulted in improved recognition performance compared to state-of-the-art techniques.