Abnormal behavior detection of human by video surveillance system
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In recent years, the number of surveillance cameras installed to monitor private and public spaces and areas has increased dramatically. There is an increasing demand for smarter video surveillance of public and private space using intelligent vision systems which can distinguish what is semantically meaningful to the human observer as „normal‟ and „abnormal‟ behaviors. Usually, the video streams are constantly recorded or observed by operators. In these cases an intelligent system can give more accurate performance than a human. In this thesis we present a video surveillance system that detects and predicts abnormal behavior of human. The system acquires color images from a stationary camera and analyzes the behavior of human. Behaviors that are common or frequent will not be given much attention by the system.