Discovering Anomalies Based on Saliency Detection and Segmentation in Surveillance System

This paper proposes extracting salient objects from motion fields. Salient object detection is an important technique for many content-based applications, but it becomes a challenging work when handling the clustered saliency maps, which cannot completely highlight salient object regions and cannot suppress background regions. We present algorithms for recognizing activity in monocular video sequences, based on discriminative gradient Random Field. Surveillance videos capture the behavioral activities of the objects accessing the surveillance system. Some behavior is frequent sequence of events and some deviate from the known frequent sequences of events. These events are termed as anomalies and may be susceptible to criminal activities. In the past, work was based on discovering the known abnormal events. Here, the unknown abnormal activities are to be detected and alerted such that early actions are taken. K. Shankar | Dr. S. Srinivasan | Dr. T. S. Sivakumaran | K. Madhavi Priya"Discovering Anomalies Based on Saliency Detection and Segmentation in Surveillance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5871.pdf http://www.ijtsrd.com/engineering/computer-engineering/5871/discovering-anomalies-based-on-saliency-detection-and-segmentation-in-surveillance-system/k-shankar