A novel method for moving object detection based on block based frame differencing

Abstract

Moving-object detection is one of the basic and most active research domains in the field of computer vision. This paper proposes a novel and efficient approach for moving object detection under a static background. Proposed approach first performs pre-processing tasks to remove noise from video frames. Secondly, it finds the difference between the current frame and previous consecutive frame as well as current frame and next consecutive frame separately. The algorithm then selects the maximum pixel intensity value between both the difference frames, which we consider as an improvement over the previous approaches. Next we divide the resultant difference frame into non-overlapping blocks and calculate the intensity sum and mean of each block. Subsequently, it finds the foreground and background pixels of each block using threshold and intensity mean. In the next step morphology operation along with connected component analysis are applied to correctly detect the target objects. The proposed approach is accurate for detecting the moving object with varying object size and numbers. This work has been formulated, implemented and tested on real video data sets and the results are found to be satisfactory as it evident from the performance analysis.