Player action recognition in broadcast tennis video with applications to semantic analysis of sports game

Zhu, Guangyu; Xu, Changsheng; Huang, Qingming; Gao, Wen; Xing, Liyuan

doi:10.1145/1180639.1180728

Cited by 67 publications

(42 citation statements)

References 39 publications

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“…Bloom and Bradley [2] detected a shot keyframe when the ball collides with the racket and determined stroke classification from heuristics based on the player and racket locations on impact. In [13], a tennis match was recorded in broadcast video and player motion was detected by extracting optical flow features. Support Vector machines (SVMs) were then used to classify the tennis strokes into either a forehand or backhand using a left-swing or a right swing class.…”

Section: Related Workmentioning

confidence: 99%

Game, shot and match: Event-based indexing of tennis

Connaghan

Kelly

O’Connor

2011

2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI)

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Section: Related Workmentioning

confidence: 99%

Game, shot and match: Event-based indexing of tennis

Connaghan

Kelly

O’Connor

2011

2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI)

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“…To identify certain events from lengthy sports video documents, most of existing approaches used audio/visual/textual features directly extracted from video content and built various models to detect event and recognize semantics [1]- [9]. These approaches can be further classified into single-modality based and multimodality based.…”

Section: ) Event Extraction Based On Video Content Onlymentioning

confidence: 99%

“…In order to improve the robustness of event detection, multimodality based approaches were employed for semantic extraction in sports video. For example, audio/visual features were utilized for highlight extraction [6], [7], and audio/visual/textual features were utilized for event detection [5], [9].…”

Section: ) Event Extraction Based On Video Content Onlymentioning

confidence: 99%

Event Tactic Analysis Based on Broadcast Sports Video

Zhu

Huang³

et al. 2009

IEEE Trans. Multimedia

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Abstract-Most existing approaches on sports video analysis have concentrated on semantic event detection. Sports professionals, however, are more interested in tactic analysis to help improve their performance. In this paper, we propose a novel approach to extract tactic information from the attack events in broadcast soccer video and present the events in a tactic mode to the coaches and sports professionals. We extract the attack events with far-view shots using the analysis and alignment of web-casting text and broadcast video. For a detected event, two tactic representations, aggregate trajectory and play region sequence, are constructed based on multi-object trajectories and field locations in the event shots. Based on the multi-object trajectories tracked in the shot, a weighted graph is constructed via the analysis of temporal-spatial interaction among the players and the ball. Using the Viterbi algorithm, the aggregate trajectory is computed based on the weighted graph. The play region sequence is obtained using the identification of the active field locations in the event based on line detection and competition network. The interactive relationship of aggregate trajectory with the information of play region and the hypothesis testing for trajectory temporal-spatial distribution are employed to discover the tactic patterns in a hierarchical coarse-to-fine framework. Extensive experiments on FIFA World Cup 2006 show that the proposed approach is highly effective.

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“…The action recognition and trajectory computing techniques introduced in [12] are employed to construct mid-level representation for broadcast tennis video in terms of visual attention space.…”

Section: Mid-level Representation For Visual Modalitymentioning

confidence: 99%

“…This approach outperforms the existing action recognition in broadcast video. More details about this approach and its comparison with the existing methods can be found in [12].…”

Section: Mid-level Representation For Visual Modalitymentioning

confidence: 99%

Highlight Ranking for Broadcast Tennis Video Based on Multi-modality Analysis and Relevance Feedback

Zhu

Huang

Gong³

2008

Advances in Multimedia Information Processing - PCM 2008

Self Cite

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Abstract. Most of existing work on sports video analysis concentrates on highlight extraction. Few efforts devoted to the important issue as how to organize the extracted highlights which is adapt for the user preference. In this paper, we propose a novel approach to rank the highlights extracted from broadcast tennis video based on multi-modality analysis and relevance feedback. Firstly, visual and auditory features are employed to construct the mid-level representations for the content of broadcast tennis video. Then, the affective features are extracted from mid-level representations and the multiple ranking models are built using nonlinear regression algorithm. Finally, the ranking models are linearly combined to generate the final highlight ranking results. The relevance feedback technique is employed to effectively capture the user interest in visual and auditory attention spaces to adjust the ranking results being suitable to the user preference. The experimental results are encouraging and demonstrate that our approach is effective.

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Player action recognition in broadcast tennis video with applications to semantic analysis of sports game

Cited by 67 publications

References 39 publications

Game, shot and match: Event-based indexing of tennis

Game, shot and match: Event-based indexing of tennis

Event Tactic Analysis Based on Broadcast Sports Video

Highlight Ranking for Broadcast Tennis Video Based on Multi-modality Analysis and Relevance Feedback

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