Contrast Media Mol Imaging. 2021 Dec 27;2021:3666622. doi: 10.1155/2021/3666622. eCollection 2021.
Visual Sequence Algorithm for Moving Object Tracking and Detection in Images.
Contrast media & molecular imaging
Renzheng Xue, Ming Liu, Xiaokun Yu
Affiliations
Affiliations
- School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang 161006, China.
- Department of Computer Science, Heilongjiang Communications Polytechnic, Qiqihar, Heilongjiang, China.
PMID: 35024011
PMCID: PMC8723875 DOI: 10.1155/2021/3666622
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
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.
Copyright © 2021 Renzheng Xue et al.
Conflict of interest statement
The authors declare that they have no conflicts of interest.
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