Vision-based behavior prediction of ball carrier in basketball matches

Abstract

A new vision-based approach was presented for predicting the behavior of the ball carrier-shooting, passing and dribbling in basketball matches. It was proposed to recognize the ball carrier’s head pose by classifying its yaw angle to determine his vision range and the court situation of the sportsman within his vision range can be further learned. In basketball match videos characterized by cluttered background, fast motion of the sportsmen and low resolution of their head images, and the covariance descriptor, were adopted to fuse multiple visual features of the head region, which can be seen as a point on the Riemannian manifold and then mapped to the tangent space. Then, the classification of head yaw angle was directly completed in this space through the trained multiclass LogitBoost. In order to describe the court situation of all sportsmen within the ball carrier’s vision range, artificial potential field (APF)-based information was introduced. Finally, the behavior of the ball carrier-shooting, passing and dribbling, was predicted using radial basis function (RBF) neural network as the classifier. Experimental results show that the average prediction accuracy of the proposed method can reach 80% on the video recorded in basketball matches, which validates its effectiveness.

Keywords

covariance descriptor / tangent space / LogitBoost / artificial potential field / radial basis function neural network

References

[1] LashkiaG., OchimachiN., NishidaE., HisamotoS.. A team play analysis support system for soccer games [C]. The 16th International Conference on Vision Interface, 2003Halifax, CanadaIAPR536-541

[2] LuW. L., OkumaK., LittleJ. J.. Tracking and recognizing actions of multiple hockey players using the boosted particle filter [J]. Image and Vision Computing, 2009, 27(1): 189-205

[3] LiF. H., WoodhamR. J.. Video analysis of hockey play in selected game situations [J]. Image and Vision Computing, 2009, 27(1): 45-58

[4] PerseM., KristanM., PersJ., MusicG., VuckovicG.. Analysis of multi-agent activity using petri nets [J]. Pattern Recognition, 2010, 43(4): 1491-1501

[5] DuqueD., SantosH., CortezP.. N-ary trees classifier [C]. Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics, 2006Setúbal, PortugalINSTICC256-261

[6] DuqueD., SantosH., CortezP.. Prediction of abnormal behaviors for intelligent video surveillance systems [C]. Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, 2007Honolulu, Hawaii, USAIEEE Press362-367

[7] CourierN., HallD., CrowleyJ. L.. Estimating face orientation from robust detection of salient facial features [C]. Int Workshop on Visual Observation of Deictic Gestures at POINTING04, 2004Cambridge, United KingdomFGnet567-576

[8] ChenI., ZhangL., HuY., LiM., ZhangH.. Head estimation using fisher manifold learning [C]. Proceeding IEEE International Workshop Analysis and Modeling of Faces and Gestures, 2003Nice, FranceIEEE Computer Society203-207

[9] ValentiR., LablackA., SebeN., DjerabaC., GeversT.. Visual gaze estimation by joint head and eye information [C]. 2010 International Conference on Pattern Recognition, 2010Istanbul, TurkeyIEEE Press3870-3874

[10] TuzelO., PorikliF., MeerP.. Pedestrian detection via classification on Riemannian manifolds [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(10): 1713-1727

[11] TuzelO., PorikliF., MeerE.. Region covariance: A fast detection and classification [J]. Proceedings of European Computer Vision, 2006, 14(2): 589-600

[12] FletcherP. T., JoshiS.. Riemannian geometry for the statistical analysis of diffusion tensor data [J]. Signal Processing, 2007, 87(2): 250-262

[13] PennecX., FillardP., AyacheN.. A Riemannian framework for tensor computing [J]. International Journal of Computer Vision, 2006, 66(1): 41-66

[14] KhatibO.. Real-time obstacle avoidance for manipulators and mobile robots [J]. The International Journal of Robotics Research, 1986, 5(1): 90-98

[15] XieL.-j., XieG.-r., ChenH.-w., LiX.-li.. Solution to reinforcement learning problems with artificial potential field [J]. Journal of Central South University: Science and Technology, 2008, 15(4): 552-557

[16] WangY. J., TuJ.Neural networks control [M], 1998BeijingMechanical Industry Publishing Company68-85

[17] BinchiniM., FrasconiP., GoriM.. Learning without local minima in radial basis function networks [J]. IEEE Transactions on Neural Networks, 1995, 6(3): 749-755

[18] WeiH.-kun.The theory and method of neural network structural design [M], 2005BeijingNational Defense Industry Press62-67