Background subtraction is one of the techniques used in video surveillance system for detecting moving objects in a video. In this paper we propose a background subtraction method which gives output even when the camera is shaking.There are many challenges that we have to consider for developing a robust background subtraction method mainly illumination variation. Here the algorithm works in such a way that the input frames are compared and compensated with reference frame then separating the foreground object with respect to background. Background subtraction can be built very efficiently on an FPGA which can process 640x480 video sequence. Experimental result shows that it is possible to detect the moving object in a very fast manner. It is implemented in Digilent Atlys Spartan-6 FPGA development board can solve various problems like complex computation, data transmission, cost of hardware resources etc. The real time video is captured by using VmodCAM.
Real time object detection and tracking is an important task in various computer vision applications. For robust object tracking the factors like object shape variation, partial and full occlusion, scene illumination variation will create significant problems. We introduce object detection and tracking approach that combines Prewitt edge detection and kalman filter. The target object's representation and the location prediction are the two major aspects for object tracking this can be achieved by using these algorithms. Here real time object tracking is developed through webcam. Experiments show that our tracking algorithm can track moving object efficiently under object deformation, occlusion and can track multiple objects.
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