Generation of Personalized Music Sports Video Using Multimodal Cues

Wang, J.; Chng, Eng Siong; Xu, Changsheng; Lu, H.; Tian, Qi

doi:10.1109/tmm.2006.888013

Cited by 35 publications

(20 citation statements)

References 18 publications

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“…Multiple trajectories of the players and the ball were extracted to construct the aggregate trajectory. The coarse and fine criteria were computed according to (19), (21), and (24). The metrics and defined in (26) and (27) were used to evaluate the performance.…”

Section: ) Results Of Interaction Pattern Recognitionmentioning

confidence: 99%

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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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Section: ) Results Of Interaction Pattern Recognitionmentioning

confidence: 99%

“…We define these nouns as "event keywords" and use software dtSearch [23] to detect them. dtSearch provides stemming, phonic, fuzzy and Boolean searching options to achieve better performance than simple word matching [24].…”

Section: A Web-casting Text Analysismentioning

confidence: 99%

Event Tactic Analysis Based on Broadcast Sports Video

Zhu

Huang³

et al. 2009

IEEE Trans. Multimedia

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“…However, the selection of these individual features is in principle performed heuristically and the efficiency of each of them has only been demonstrated in specific application cases. On the contrary, the MFCC coefficients provide a more complete representation of the audio characteristics and their efficiency has been proven in numerous and diverse application domains [40][41][42][43][44]. Taking into account the aforementioned facts, while also considering that this work aims at adopting common techniques of the literature for realizing generic audio-based shot classification, only the MFCC coefficients are considered in the proposed analysis framework.…”

Section: Color-and Audio-based Analysismentioning

confidence: 99%

Joint modality fusion and temporal context exploitation for semantic video analysis

Papadopoulos

Mezaris

Kompatsiaris

et al. 2011

EURASIP J. Adv. Signal Process.

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In this paper, a multi-modal context-aware approach to semantic video analysis is presented. Overall, the examined video sequence is initially segmented into shots and for every resulting shot appropriate color, motion and audio features are extracted. Then, Hidden Markov Models (HMMs) are employed for performing an initial association of each shot with the semantic classes that are of interest separately for each modality. Subsequently, a graphical modeling-based approach is proposed for jointly performing modality fusion and temporal context exploitation. Novelties of this work include the combined use of contextual information and multi-modal fusion, and the development of a new representation for providing motion distribution information to HMMs. Specifically, an integrated Bayesian Network is introduced for simultaneously performing information fusion of the individual modality analysis results and exploitation of temporal context, contrary to the usual practice of performing each task separately. Contextual information is in the form of temporal relations among the supported classes. Additionally, a new computationally efficient method for providing motion energy distribution-related information to HMMs, which supports the incorporation of motion characteristics from previous frames to the currently examined one, is presented. The final outcome of this overall video analysis framework is the association of a semantic class with every shot. Experimental results as well as comparative evaluation from the application of the proposed approach to four datasets belonging to the domains of tennis, news and volleyball broadcast video are presented.

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“…Because of the enormous difference in sports videos, sport specific methods show successful results and thus constitute the majority of work. Some of the genre specific researches have been done in soccer (football) [3], [4] tennis [5], cricket [6], basketball [7], volleyball [8], etc. less work is observed for genre-independent studies [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Wicket Fall Concept Mining From Cricket Video Using A-Priori Algorithm

Goyani¹,

Dutta²,

Gohil³

et al. 2011

IJMA

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In this paper, we propose an algorithm to detect semantic concepts from cricket video. In our previous work, we have proposed key frame detection based approach for semantic event detection and classification. The proposed scheme works in two parts. In first part a top-down event detection and classification is performed using hierarchical tree. In second part, higher level concept is identified by applying A-Priori algorithm. In part 1, key frames are identified based on Hue Histogram difference at level 1. At level 2, logo transitions classify the frames as real-time or replay. At level 3, we classify the real time frames as field view, pitch view or non field view based on thresholds like Dominant Soli Pixel Ration (DSPR) and Dominant Grass Pixel Ration (DGPR). At level 4, we detect close up and crowd frames based upon edge detection. At level 5a, we classify the close up frames into player of team

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Generation of Personalized Music Sports Video Using Multimodal Cues

Cited by 35 publications

References 18 publications

Event Tactic Analysis Based on Broadcast Sports Video

Event Tactic Analysis Based on Broadcast Sports Video

Joint modality fusion and temporal context exploitation for semantic video analysis

Wicket Fall Concept Mining From Cricket Video Using A-Priori Algorithm

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