Motion trajectory tracking of athletes with improved depth information-based KCF tracking method

Zhang, Lina; Dai, Haidong

doi:10.1007/s11042-023-14929-6

Cited by 3 publications

(1 citation statement)

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“…The evaluation metrics for clustering performance included Average Recall (R), Average Precision (P), and the area under the curve (AUC) for both micro-average (micro-AUC) and macro-average (macro-AUC) [27]. The formulas for calculating Average Recall and Average Precision are given by ( 7) and (8):…”

Section: Wfmentioning

confidence: 99%

Automatic Shuttlecock Motion Recognition Using Deep Learning

Zhao

2023

IEEE Access

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In the field of sports video processing, specifically in the context of motion recognition for shuttlecock match videos, we first propose a method based on attitude estimation to measure the movement extent of the shotting arm, allowing for temporal positioning of shuttlecock shot motions and extraction of corresponding shot-sequences. The shot-sequences is defined in this paper as video segments that exclusively contain the complete shot motion of the mainplayer. During the training phase, we incorporate a lightweight channel-spatial attention mechanism into the Temporal Segment Network (TSN) to classify the extracted shot-sequences into four types, i.e., forehand shot, backhand shot, smash shot, and drop shot. Furthermore, we employ image morphology-based techniques to further distinguish smash shot-sequences as either high clear shots or kill shots based on the shuttlecock's motion trajectory. The extensive experiment results demonstrate the effectiveness of proposed method in accurately positioning and recognizing shuttlecock shot motions.

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Section: Wfmentioning

confidence: 99%