Motion Detection Using Three Frame Differencing and CNN

The three-frame difference is a renowned tactic for detecting moving items. According to the idea, the existence of a moving object can be inferred by removing three subsequent image frames that display the moving object’s edges. However, information is lost as a result of the method because these edges do not convey all of the information about the moving item. To obtain all of the information about a moving object, post-processing techniques like morphological operations, optical flow, and combining these techniques must be used. In this study, we introduce a novel method to detect moving objects in video sequences without any post-processing steps, dubbed Selected Three Frame Difference (STFD). We first provide an algorithm that selects three images while accounting for the local maximum value of frame disparities rather than employing successive. The logical operator is applied to three frames of three different picture variations that contain non-overlapping object frames. We mathematically show that the entire moving object is always discernible in the second image that was selected. We investigated the proposed strategy on a dataset collected in our lab and a public benchmark dataset. We compared the effectiveness of our approach to the three-frame difference method and background subtraction-based traditional moving object recognition methods on a few sample videos selected from different datasets.