Abnormal behaviour detection at traffic junctions using Lucas Kanade and Harris Corner detector

Image processing has played a vital role in every aspect of human life. Video surveillance has reached a major out through by the application of advanced image processing and frame modeling techniques. Video surveillance is the most recent issue regarding community security and welfare. Detection of behavior of moving objects in video streams is a vital aspect. In this article, automatic real-time object's behavior detection is implemented using Lucas Kanade and Harris Corner based approach. The velocity of moving element is reported and their association with anomalous activity is also an inseparable part of this art. This work can be used to develop a surveillance system of static camera and robotic automation visual systems. Whenever a new object comes in the camera frame, the system uses the concepts of frame based processing using Lucas Kanade approach incorporated with Harris Corner Detector. In this work, an investigation is carried out to define the optimum value of velocity parameters for declaring a behavior as anomalous. Moreover, it is analyzed that proposed algorithm for video behaviour detection at traffic points perform well but its accuracy can be further enhanced using three dimensional imaging and mapping of moving elements in a hyperspace.