Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

Abstract

Objective. The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods. An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results. The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion. The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

Research on detecting moving targets with an improved Kalman filter algorithm

“…which was simulated by initial alignment and compared with the conventional Kalman filter, the error accuracy of the misalignment angle is improved by an order of magnitude [26]. In a paper, Xue Renzheng mentioned the improved frame difference method to detect automatically the moving object's automatic detection and tracking algorithm in a quick a precise way [27]. Bissin et al have studied the detection of moving objects.…”