A Generic Approach for Systematic Analysis of Sports Videos

Ning Zhang, Ling-Yu Duan, Lingfang Li, Qingming Huang, Jun Du, Wen Gao, Ling Guan
2012 ACM Transactions on Intelligent Systems and Technology  
Various innovative and original works have been applied and proposed in the field of sports video analysis. However, individual works focused on sophisticated methodologies with particular sport types and there was a lack of scalable and holistic framework in this field. This paper proposes a solution for this issue and presents a systematic and generic approach which is experimented on a relatively large-scale sports consortia. The system aims at the event detection scenario of an input video
more » ... ith an orderly sequential process. Initially, domain-knowledge independent local descriptors are extracted homogeneously from the input video sequence. Then the video representation is created by adopting a bag-of-visual-words (BoW) model. The video's genre is firstly identified by applying the k-nearest neighbor (k-NN) classifiers on the initially obtained video representation, with various dissimilarity measures are assessed and evaluated analytically. Subsequently, an unsupervised probabilistic latent semantic analysis (PLSA) based approach is employed at the same histogram-based video representation, in characterizing each frame of video sequence into one of four view groups, namely closed-up-view, mid-view, long-view and outer-fieldview. Finally, A hidden conditional random field (HCRF) structured prediction model is utilized for interesting event detection. From experimental results, k-NN classifier using KL-divergence measurement demonstrates the best accuracy at 82.16% for genre categorization. Supervised SVM and unsupervised PLSA have average classification accuracies at 82.86% and 68.13%, respectively. The HCRF model achieves 92.31% accuracy using the unsupervised PLSA based label input, which is comparable with the supervised SVM based input at an accuracy of 93.08%. In general, such a systematic approach can be widely applied in processing massive videos generically. This article extends the previous work by the authors appearing under the title "Automatic sports genre categorization and view-type classification over large-scale dataset," ].
doi:10.1145/2168752.2168760 fatcat:oxqszt2dxzcdfa44tlapet3kje