| dc.description.abstract | The closed circuit television（CCT） has been used instead of human eyes in the past few decades. The main function is only on the recording of events. A caretaker who has to watch ten or twenty monitors attentively and simultaneously day and night to prevent illegal entries. It is an intensive burden work for human being to carry on. Recently, computer-based cameras have been widely used because of the low cost and vast storage. More importantly, the technologies artificial intelligence, video processing, and pattern recognition have been successfully developed for digital video signals. Thus, an intelligent video surveillance and monitoring （VSAM）system gradually becomes the key role in the security systems of buildings, companies or campus.
Conventionally, motion detection, target tracking, and target classification are the main research topics in many constructed VSAM systems. However, the ultimate goal of surveillance systems is to identify the objects like the ID of individuals. In this thesis, we will develop an intelligent VSAM system to increase the identification power by using the biometrics features, such as fingerprint, palm-print, face, gesture, etc.
In the proposed system, the individuals are first identified in the video streams. Motion detection and target tracking are then accomplished. Last, the target classification of persons is achieved by using the biometric gait features. The system is first implemented in indoor and controlled environment. Then, this system is extended to the complex environments such as outdoor with the clutter background. Experimental results verify the validity of the proposed system. | en_US |