Abstract
Mean-Shift is known for its real time and robustness in visual tracking. This is a very good algorithm. In recent years, the algorithm has developed rapidly and has great development prospects. This paper studies Mean-Shift theory and target tracking theory and analyses its advantages in video tracking. By combining the three-frame difference method, nearest neighbour method, and the direction parameter of the target, the Mean-Shift theory based on kernel probability density estimation is studied in the video application in moving target recognition and tracking in sequence images. First, use the prediction method to initially locate the target’s position, and then use the Mean-Shift algorithm to perform iterative calculation to determine the true position of the target. Experiments show that the Mean-Shift algorithm avoids global search, and the improved algorithm introduces the number of iterations, reduces the computational complexity of the algorithm, reduces the time consumption, and ensures the real-time tracking of the algorithm.
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04 October 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s11227-022-04863-z
References
Saha A, Lee YW, Hwang YS et al (2018) Context-aware block-based motion estimation algorithm for multimedia internet of things (IoT) platform. Pers Ubiquit Comput 22(7):1–10
Jia Danping, Zhang Lifeng, Li Chunhua (2015) The improvement of mean-shift algorithm in target tracking. Int J Secur Appl 9(2):21–28
Ghassabeh YA (2015) A sufficient condition for the convergence of the Mean-Shift algorithm with Gaussian kernel. J Multivar Anal 135:1–10
Fang H, Chen Y, Li L et al (2018) Implementation of the parallel Mean-Shift-based image segmentation algorithm on a GPU cluster. Int J Digit Earth 5:1–26
Qin Zhen, Shelton Christian R (2016) Social grouping for multi-target tracking and head pose estimation in video. IEEE Trans Pattern Anal Mach Intell 38(10):2082–2095
Dixit Astha, Verma Manoj, Patidar Kailash (2016) Survey on video object detection and tracking. Int J Curr Trends Eng Technol 2(2):264–268
Borisova IV, Legkiy VN, Kravets SA (2017) Application of the gradient orientation for systems of automatic target detection. Novosibirsk State Tech Univ 41(6):931–937
He J, Gao H, Mu H et al (2017) Rapid detection of quality parameters change in hickory oxidation process by electronic nose. Trans Chin Soc Agric Eng 33(14):284–291
Zhao LX, Su Q, Liu H et al (2014) Application of flexible edge matching algorithm in the field of moving object detection. Intell Autom Soft Comput 20(4):515–523
Li G, Wang ZY, Jian L et al (2018) Spatio-context-based target tracking with adaptive multi-feature fusion for real-world hazy scenes. Cognit Comput 7:1–13
Li F, Peng H, Sun X et al (2012) Wave propagation analysis in composite laminates containing a delamination using a three-dimensional spectral element method. Math Probl Eng 2012(2):243–253
Liu K, Wei S, Chen Z et al (2017) A real-time high performance computation architecture for multiple moving target tracking based on wide-area motion imagery via cloud and graphic processing units. Sensors 17(2):356
Xue H, Yao L, Deng C et al (2016) Tracking people in RGBD videos using deep learning and motion clues. Neurocomputing 204:70–76
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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s11227-022-04863-z
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Hui, Q. RETRACTED ARTICLE: Motion video tracking technology in sports training based on Mean-Shift algorithm. J Supercomput 75, 6021–6037 (2019). https://doi.org/10.1007/s11227-019-02898-3
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DOI: https://doi.org/10.1007/s11227-019-02898-3