2019
DOI: 10.1117/1.jei.28.3.033037
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Adaptive patch feature matching and scale estimation for visual object tracking

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Cited by 3 publications
(1 citation statement)
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“…In formula ( 14), r 1 represents the state correlation estimated feature value of the volleyball player's arm trajectory, r 2 represents the correlation coefficient, σ 1 represents the feature matching degree, and N 1 is the linear component of the volleyball player's arm trajectory. Set h as the edge pixel set of volleyball player's arm hitting trajectory, use the adaptive block feature matching method to segment the image contour of volleyball player's arm hitting trajectory [32][33][34], divide the chromatographic image into M × N subblocks G m,n of 2 × 2, and obtain the feature distribution matrix of volleyball player's arm hitting trajectory tracking as follows:…”
Section: Pretreatment Of Volleyball Player's Arm Hittingmentioning
confidence: 99%
“…In formula ( 14), r 1 represents the state correlation estimated feature value of the volleyball player's arm trajectory, r 2 represents the correlation coefficient, σ 1 represents the feature matching degree, and N 1 is the linear component of the volleyball player's arm trajectory. Set h as the edge pixel set of volleyball player's arm hitting trajectory, use the adaptive block feature matching method to segment the image contour of volleyball player's arm hitting trajectory [32][33][34], divide the chromatographic image into M × N subblocks G m,n of 2 × 2, and obtain the feature distribution matrix of volleyball player's arm hitting trajectory tracking as follows:…”
Section: Pretreatment Of Volleyball Player's Arm Hittingmentioning
confidence: 99%