A framework for abandoned object detection from video surveillance

In this paper, we propose a method to detect abandoned object from surveillance video. In first step, foreground objects are extracted using background subtraction in which background modeling is done through running average method. In second step, static objects are detected by using contour features of foreground objects of consecutive frames. In third step, detected static objects are classified into human and non-human objects by using edge based object recognition method which is capable to generate the score for full or partial visible object. Nonhuman static object is analyzed to detect abandoned object. Experimental results show that proposed system is efficient and effective for real-time video surveillance, which is tested on IEEE Performance Evaluation of Tracking and Surveillance data set (PETS 2006, PETS 2007) and our own dataset.