Performance Evaluation of Various Foreground Extraction Algorithms for Object detection in Visual Surveillance
Sudheer Reddy Bandi · 2012
Detecting moving objects in video sequence with a lot of moving vehicles and other difficult conditions is a fundamental and difficult task in many computer vision applications. A common approach is based on background subtraction, which identifies moving objects from the input video frames that differs significantly from the background model. Numerous approaches to this problem differs in the type of background modeling technique and the procedure to update the model. In this paper, we have analysed three different background modeling techniques namely median, change detection mask and histogram based modeling technique and two background subtraction algorithms namely frame difference and approximate median. For all possible combinations of algorithms on various test videos we compared the efficiency and found that background modeling using median value and background subtraction using frame difference is very robust and efficient.