Mean-shift Object Tracking Algorithm with Systematic Sampling Technique

Abstract

Mean shift is a fast object tracking algorithm that only considers pixels in an object area, hence its relatively small computational load. This algorithm is suitable for use in real-time conditions in terms of execution time. The use of histograms causes this algorithm to be relatively resistant to rotation and changes in object size. However, its resistance to lighting changes is not optimal. This study aims to improve the performance of the algorithm under lighting changes and reduce its processing time. The proposed technique involves the use of sampling techniques to reduce the number of iterations, optimization of candidate search object locations using simulated annealing, and addition of tolerance parameter to optimize object location search and area-based weighting instead of the Epanechnikov kernel. The results of the one-tail t-test with two independent sample groups reveal that the average performance of the proposed algorithm is significantly better than that of the traditional mean-shift algorithm in terms of resistance to lighting changes and processing time per video frame. In the test involving 999 frames of video images, the average processing time of the proposed algorithm is 83.66 ms, whereas that of the traditional mean-shift algorithm is 116.86 ms.