Abstract
The presence of standard video editing practices in broadcast sports videos, like football, effectively means that such videos have stronger contextual priors than most generic videos. In this paper, we show that such information can be harnessed for automatic analysis of sports videos. Specifically, given an input video, we output per-frame information about camera angles and the events (goal, foul, etc.). Our main insight is that in the presence of temporal context (camera angles) for a video, the problem of event tagging (fouls, corners, goals, etc.) can be cast as per frame multi-class classification problem. We show that even with simple classifiers like linear SVM, we get significant improvement in the event tagging task when contextual information is included. We present extensive results for 10 matches from the recently concluded Football World Cup, to demonstrate the effectiveness of our approach.
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Assfalg, J., Bertini, M., Colombo, C., Bimbo, A.D., Nunziati, W.: Semantic annotation of soccer videos: automatic highlights identification. CVIU 92, 285–305 (2003)
Chen, C., Wang, O., Heinzle, S., Carr, P., Smolic, A., Gross, M.: Computational sports broadcasting: automated director assistance for live sports. In: ICME (2013)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, ECCV, vol. 1, pp. 1–2 (2004)
Duan, L.Y., Xu, M., Tian, Q., Xu, C.S., Jin, J.S.: A unified framework for semantic shot classification in sports video. Multimed. IEEE Trans. 7(6), 1066–1083 (2005)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. TPAMI 32(9), 1627–1645 (2010)
Heckbert, P.: Color image quantization for frame buffer display. In: SIGGRAPH (1982)
Jain, V., Singhal, A., Luo, J.: Selective hidden random fields: exploiting domain-specific saliency for event classification. In: CVPR (2008)
Kapela, R., McGuinness, K., Swietlicka, A., OConnor, N.E.: Real-time event detection in field sport videos. In: Computer vision in Sports, pp. 293–316 (2014)
Kim, K., Grundmann, M., Shamir, A., Matthews, I., Hodgins, J., Essa, I.: Motion fields to predict play evolution in dynamic sport scenes. In: CVPR (2010)
Kong, Y., Hu, W., Zhang, X., Wang, H., Jia, Y.: Learning group activity in soccer videos from local motion. In: ACCV (2010)
Koprinska, I., Carrato, S.: Temporal video segmentation: a survey. Signal Process.: Image Commun. 16(5), 477–500 (2001)
Ladicky, L., Russell, C., Kohli, P., Torr, P.H.: Graph cut based inference with co-occurrence statistics. In: ECCV (2010)
Lucey, P., Bialkowski, A., Carr, P., Morgan, S., Matthews, I., Sheikh, Y.: Representing and discovering adversarial team behaviors using player roles. In: CVPR (2013)
Ma, Z., Yang, Y., Cai, Y., Sebe, N., Hauptmann, A.G.: Knowledge adaptation for ad hoc multimedia event detection with few exemplars. In: ACM Multimedia, pp. 469–478 (2012)
Nguyen, N., Yoshitaka, A.: Shot type and replay detection for soccer video parsing. In: Multimedia (ISM), 2012 IEEE International Symposium on, pp. 344–347 (2012)
Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: CVPR (2011)
Qian, X., Liu, G., Wang, Z., Li, Z., Wang, H.: Highlight events detection in soccer video using hcrf. In: Proceedings of the Second International Conference on Internet Multimedia Computing and Service, pp. 171–174 (2010)
Sigari, M.H., Soltanian-Zadeh, H., Kiani, V., Pourreza, A.R.: Counterattack detection in broadcast soccer videos using camera motion estimation. In: AISP, pp. 101–106 (2015)
Thompson, R., Bowen, C.: Grammar of the Edit. Focal Press, Massachusetts (2009)
Walecki, R., Rudovic, O., Pavlovic, V., Pantic, M.: Variable-state latent conditional random fields for facial expression recognition and action unit detection. In: Automatic Face and Gesture Recognition (2015)
Wang, L., Qiao, Y., Tang, X.: Action recognition with trajectory-pooled deep-convolutional descriptors. In: CVPR (2015)
Xie, L., Chang, S.F., Divakaran, A., Sun, H.: Structure analysis of soccer video with hidden Markov models. In: ICASSP (2002)
Xu, C., Wang, J., Wan, K., Li, Y., Duan, L.: Live sports event detection based on broadcast video and web-casting text. In: ACM Multimedia, pp. 221–230 (2006)
Xu, G., Ma, Y.F., Zhang, H.J., Yang, S.Q.: An hmm-based framework for video semantic analysis. Circuits Syst. Video Technol. IEEE Trans. 15(11), 1422–1433 (2005)
Xu, P., Xie, L., Chang, S.F., Divakaran, A., Vetro, A., Sun, H.: Algorithms and system for segmentation and structure analysis in soccer video. ICME 1, 928–931 (2001)
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Sharma, R.A., Gandhi, V., Chari, V. et al. Automatic analysis of broadcast football videos using contextual priors. SIViP 11, 171–178 (2017). https://doi.org/10.1007/s11760-016-0916-3
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DOI: https://doi.org/10.1007/s11760-016-0916-3