Moving object tracking using Laplacian-DCT based perceptual hash
Sandeep Singh Sengar, Susanta Mukhopadhyay · 2016
Moving-object tracking is one of the basic and hot research domains in the computer vision area. This work presents a novel and effective method to track moving objects under a static background. Proposed method first executes the pre-processing tasks to remove noise from video frames. Then, with the help of rectangular window, we select the target object region in the first video frame (reference frame). Next, it applies the Laplacian operator on the selected target objects for sharpening and edge detection. The algorithm then applies the DCT and selects the few high energy coefficients. Subsequently, it computes the perceptual hash of the selected target objects with the help of mean of all the AC values of the block. Using perceptual hash of a target object, we find the similar object in subsequent frames of the video. The proposed method is correct for tracking the moving target object with varying object size and significant amount of noise. This work has been formulated, implemented and tested on real indoor-outdoor video sequences and the results are found to be adequate as it proved from the performance evaluation.