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      Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

      research-article
      1 , , 1 , 2
      Contrast Media & Molecular Imaging
      Hindawi

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          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.

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          Most cited references18

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          Robust and autonomous stereo visual-inertial navigation for non-holonomic mobile robots

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            Correlation Filter Selection for Visual Tracking Using Reinforcement Learning

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              Fusing geometrical and visual information via superpoints for the semantic segmentation of 3D road scenes

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                Author and article information

                Contributors
                Journal
                Contrast Media Mol Imaging
                Contrast Media Mol Imaging
                CMMI
                Contrast Media & Molecular Imaging
                Hindawi
                1555-4309
                1555-4317
                2021
                27 December 2021
                : 2021
                Affiliations
                1School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang 161006, China
                2Department of Computer Science, Heilongjiang Communications Polytechnic, Qiqihar, Heilongjiang, China
                Author notes

                Academic Editor: Yuvaraja Teekaraman

                Article
                10.1155/2021/3666622
                8723875
                166cd19e-f27a-428e-bd90-dbb8752bbc7e
                Copyright © 2021 Renzheng Xue et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Funding
                Funded by: Heilongjiang Province Education Department Scientific Research Project
                Award ID: 135409224
                Categories
                Research Article

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