Abstract
With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activities, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation of the performance of human activity recognition.
Highlights
After the tragic event on September 11 and the subsequent terrorist attacks around the world, visual surveillance has attracted much more attention and has been adopted in different applications for crime detection and prediction
Our emphasis in this paper aims to discuss the existing high-level techniques, and provide summary of progress achieved in the direction of building robust and intelligent vision based methods, including abnormal activity templates, abnormal activity models, and manifold geometry
Evaluating the performance of methods and systems for activity recognition in videos or image sequences is important for the improvement of surveillance algorithms in theory, and for the selection of proper surveillance solutions towards practical applications
Summary
After the tragic event on September 11 and the subsequent terrorist attacks around the world, visual surveillance has attracted much more attention and has been adopted in different applications for crime detection and prediction. A large number of in-depth research papers have been published on the recognition and understanding of human activities. They can be classified into two types of approaches: active techniques and passive techniques. The commercial products such as the Nintendo’s WII or Microsoft’s Kinect are good examples that make use of active techniques [1]. Such products have been partially successful, their deployment per location is usually not practical in widespread public areas
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