Intelligent Visual Surveillance Based On Human Action Recognition

The intelligent visual surveillance has attracted significant interest in recent years. Some intelligent visual surveillance product has appeared in public areas as well. However most of the products are based on motion object detecting and tracking. They are not intelligent enough. In this paper, we propose an intelligent visual surveillance solutions based on the human action recognition.There are three important approaches, human detection, tracking and action classifying, in human action recognition. Recently, the approaches of human detection and tracking are efficient enough. So we focus on the approach of human action recognition classifying. We complete the basic algorithm of the human detection and tracking and present an improved approach to classify human action based on the BOW model and the pLSA (probabilistic Latent Semantic Analysis) model. We propose an improved feature, which is called the local spatial-temporal maximum value of optical flow to build our bag of words. This feature is able to reduce the high dimension of the pure optical flow template and also has abundant motion information. Our approach of recognition is tested on two datasets, the KTH datasets and WEIZMANN datasets. The result shows its good performance.In order to test our method in reality, we develop an intelligent visual surveillance platform. This platform has basic function of human detection and tracking. The human action classifying algorithm based on our improved approach and a method based on the projection histogram are also in the platform. The surveillance information can be sent to the mobile phone automatically. Compared to the common intelligent visual surveillance platform, our intelligent visual surveillance platform is more intelligent and effective.