Abstract
In this article, we present a method to perform automatic player trajectories mapping based on player detection, unsupervised labeling, efficient multi-object tracking, and playfield registration in broadcast soccer videos. Player detector determines the players' positions and scales by combining the ability of dominant color based background subtraction and a boosting detector with Haar features. We first learn the dominant color with accumulate color histogram at the beginning of processing, then use the player detector to collect hundreds of player samples, and learn player appearance codebook by unsupervised clustering. In a soccer game, a player can be labeled as one of four categories: two teams, referee or outlier. The learning capability enables the method to be generalized well to different videos without any manual initialization. With the dominant color and player appearance model, we can locate and label each player. After that, we perform multi-object tracking by using Markov Chain Monte Carlo (MCMC) data association to generate player trajectories. Some data driven dynamics are proposed to improve the Markov chain's efficiency, such as label consistency, motion consistency, and track length, etc. Finally, we extract key-points and find the mapping from an image plane to the standard field model, and then map players' position and trajectories to the field. A large quantity of experimental results on FIFA World Cup 2006 videos demonstrate that this method can reach high detection and labeling precision, reliably tracking in scenes of player occlusion, moderate camera motion and pose variation, and yield promising field registration results.
- Bar-Shalom, Y. and Fortmann, T. 1998. Tracking and Data Association. Academic Press, San Diego, CA. Google ScholarDigital Library
- Bebie, T. and Bieri, H. 2000. A video-based 3D-reconstriction of soccer games. Proc. Eurographics, 19, 3.Google Scholar
- Bilmes, J. A. 1998. A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden Markov models. Tech. rep., TR-97--021, Berkeley.Google Scholar
- Choi, K. and Seo, Y. 2004. Probabilistic tracking of soccer players and ball. In Proceedings of the International Workshop on Statistical Methods in Video.Google Scholar
- Farin, D., Han, J., and With, P. 2005. Fast camera calibration for the analysis of sport sequence. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME). 80--91.Google Scholar
- Farin, D., Krabbe, S., With, P., and Effelsberg, W. 2004. Robust camera calibration for sport videos using court models. In Proceedings of the SPIE Storage Retr. Meth. Appl. Multimed. vol. 5307, 80--91.Google Scholar
- Fischler, M. A. and Bolles, R. C. 1981. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24, 381--395. Google ScholarDigital Library
- Hough, P. V. C. 1959. Machine analysis of bubble chamber pictures. In Proceedings of the International Conference on High Energy Accelerators and Instrumentation. CERN.Google Scholar
- Isard, M. and MacCormick, J. 2001. BraMBLe: A bayesian multiple-blob tracker. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).Google ScholarCross Ref
- Jiang, S., Huang, Q., and Gao, W. 2007. Mining information of attack-defense status from soccer video based on scene analysis. In Proceedings of the IEEE International Conference on Multimedia & Expo (ICME).Google Scholar
- Kaucic, R., Perera, A., Brooksby, G., Kaufhold, J., and Hoogs, A. 2005. A unified framework for tracking through occlusions and across sensor gaps. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarDigital Library
- Kim, T., Seo, Y., and Hong, K. 1998. Physics-based 3D position analysis of a soccer ball from monocular image sequences. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). Google ScholarDigital Library
- Leibe, B., Seemann, E., and Schiele, B. 2005. Pedestrian detection in crowded scenes. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarDigital Library
- Li, J., Wang, T., Hu, W., and Zhang, Y. 2006. Soccer highlight detection using two-dependent Bayesian network. In Proceedings of the IEEE International Conference on Multimedia & Expo (ICME).Google Scholar
- Li, W., Tong, X., and Zhang, Y. 2007. Optimization and parallelization on a multimedia application. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME).Google Scholar
- Liu, J. S. 2001. Monte Carlo strategies in scientific computing. Springer. Google ScholarDigital Library
- Liu, Y., Huang, Q., Ye, Q., and Gao, W. 2005. A new method to calculate the camera focusing area and player position on playfield in soccer video. In Proceedings of the SPIE Visual Communications and Image Processing.Google Scholar
- Lowe, D. G. 1999. Object recognition from local scale-invariant features. In Proceedings of the International Conference on Computer Vision. 1150--1157. Google ScholarDigital Library
- Nillius, P., Sullivan, J., and Carlsson, S. 2006. Multi-target tracking—Linking identities using Bayesian network inference. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarDigital Library
- Oh, S., Russell, S., and Sastry, S. 2005. Markov chain Monte Carlo data association for multiple-target tracking. Tech. rep. UCB//ERL M05/19, University of California, Berkeley.Google Scholar
- Okuma, K., Taleghani, A., Freitas, N., Little, J., and Lowe, D. G. 2004. A boosted particle filter: Multitarget detection and tracking. In Proceedings of the European Conference on Computer Vision (ECCV).Google Scholar
- Perera, A., Srinivas, C., Hoogs, A., Brooksby, G., and Hu, W. 2006. Multi-object tracking through simultaneous long occlusions and split-merge conditions. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarDigital Library
- Peursum, P., Venkatesh, S., West, G. A. W., and Bui, H. H. 2003. Object labelling from human action recognition. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom). Google ScholarDigital Library
- Reid, D. 1979. An algorithm for tracking multiple targets. IEEE Trans. Automat. Cont. 24, 6, 843--854.Google ScholarCross Ref
- Sato, K. and Aggarwal, J. K. 2005. Tracking soccer players using broadcast TV images. In Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).Google Scholar
- Shotton, J., Winn, J., Rother, C., and Criminisi, A. 2006. TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In Proceedings of the European Conference on Computer Vision (ECCV). Google ScholarDigital Library
- Stauffer, C. 2003. Estimating track sources and sinks. In Proceedings of the IEEE Workshop on Event Mining in Video.Google Scholar
- Sullivan, J. and Carlsson, S. 2006. Tracking and labeling of interacting multiple targets. In Proceedings of the European Conference on Computer Vision (ECCV). Google ScholarDigital Library
- Thomas, G. 2007. Real-time camera tracking using sports pitch markings. J. Real-Time Image Proc. 2, 2--3.Google ScholarCross Ref
- Tong, X., Wang, T., Li, W., and Zhang, Y. 2007. A three-level scheme for real-time ball tracking. In Proceedings of the International Workshop on Multimedia Content Analysis and Mining. Google ScholarDigital Library
- Viola, P. and Jones, M. 2001. Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Wang, L., Zeng, B., Lin, S., Xu, G., and Shum, H.-Y. 2004. Automatic extraction of semantic colors in sports video. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).Google Scholar
- Watanabe, T., Haseyama, M., and Kitajima, H. 2004. A soccer field tracking method with wire frame model from TV images. In Proceedings of the IEEE International Conference on Image Processing (ICIP).Google Scholar
- Xu, D. and Chang, S-F. 2007. Visual event recognition in news video using kernel methods with multi-level temporal alignment. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref
- Yilmaz, A., Javed, O., and Shah, M. 2006. Object tracking—A survey. ACM Comp. Surv. Google ScholarDigital Library
- Yu, Q. and Medioni, G. 2007. Map-enhanced detection and tracking from a moving platform with local and global data association. In Proceedings of the IEEE Workshop on Motion and Video Computing (WMVC). Google ScholarDigital Library
- Yu, Q., Medioni, G., and Cohen, I. 2007. Multiple target tracking using spatio-temporal markov chain monte carlo data association. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Yu, X., Yan, X., Hay, T., and Leong, H. 2004. 3D Reconstruction and enrichment of broadcast soccer video. In Proceedings of the 12th Annual ACM International Conference on Multimedia. Google ScholarDigital Library
- Zhang, D., Gatica-Perez, D., Bengio, S., and McCowan, I. 2005. Semi-supervised adapted HMMs for unusual event detection. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). Google ScholarDigital Library
Index Terms
- Automatic player labeling, tracking and field registration and trajectory mapping in broadcast soccer video
Recommendations
Automatic player detection, tracking and mapping to field model for broadcast soccer videos
MoMM '11: Proceedings of the 9th International Conference on Advances in Mobile Computing and MultimediaWe present an automatic soccer analysis framework through detecting, tracking players, and reflecting the information into the field model. First, a player detector is built on four-seed edge feature approach which extracts player regions applied in ...
Automatic player detection, labeling and tracking in broadcast soccer video
In this paper, we present a method to perform automatic multiple player detection, unsupervised labeling and efficient tracking in broadcast soccer videos. Player detection is to determine the players' positions and scales. It is achieved by combining ...
Recognizing tactic patterns in broadcast basketball video using player trajectory
The explosive growth of the sports fandom inspires much research on manifold sports video analyses and applications. The audience, sports fans, and even professionals require more than traditional highlight extraction or semantic summarization. Computer-...
Comments