Player trajectory reconstruction for tactical analysis

To increase the performance of sport team, the tactical analysis of team from game video is essential. Trajectories of the players are the most useful cues in a sport video for tactical analysis. In this paper, we propose a technique to reconstruct the trajectories of players from broadcast basketball videos. We first propose a mosaic based approach to detect the boundary lines of court. Then, the locations of players are determined by the integration of shape and color visual information. A layered graph is constructed for the detected players, which includes all possible trajectories. A dynamic programming based algorithm is applied to find the trajectory of each player. Finally, the trajectories of players are displayed on a standard basketball court model by a homography transformation. In contrast to related works, our approach exploits more spatio-temporal information in video. Experimental results show that the proposed approach works well and outperforms some existing technique.

[1]  Min-Chun Hu,et al.  Robust Camera Calibration and Player Tracking in Broadcast Basketball Video , 2011, IEEE Transactions on Multimedia.

[2]  Hua-Tsung Chen,et al.  Shot Classification of Basketball Videos and its Application in Shooting Position Extraction , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[3]  Michal Irani,et al.  Video indexing based on mosaic representations , 1998, Proc. IEEE.

[4]  A. Murat Tekalp,et al.  Robust Dominant Color Region Detection with Applications to Sports Video Analysis ? , 2002 .

[5]  Huchuan Lu,et al.  Deep visual tracking: Review and experimental comparison , 2018, Pattern Recognit..

[6]  Fabio Tozeto Ramos,et al.  Simple online and realtime tracking , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[7]  Ulrich Rückert,et al.  A Computer Vision Based Tracking System for Indoor Team Sports , 2009, ICIC 2009.

[8]  David V. Thiel,et al.  Team Player Tracking Using Sensors and Signal Strength for Indoor Basketball , 2016, IEEE Sensors Journal.

[9]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[10]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Moncef Gabbouj,et al.  Salient Event Detection in Basketball Mobile Videos , 2014, 2014 IEEE International Symposium on Multimedia.

[12]  Chien-Li Chou,et al.  Recognizing tactic patterns in broadcast basketball video using player trajectory , 2012, J. Vis. Commun. Image Represent..

[13]  Yannick Boursier,et al.  Sport players detection and tracking with a mixed network of planar and omnidirectional cameras , 2009, 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC).

[14]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[16]  Whoi-Yul Kim,et al.  Highlight generation for basketball video using probabilistic excitement , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[17]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Changsheng Xu,et al.  Semantic Event Extraction from Basketball Games using Multi-Modal Analysis , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[19]  James J. Little,et al.  Learning to Track and Identify Players from Broadcast Sports Videos , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Dietrich Paulus,et al.  Simple online and realtime tracking with a deep association metric , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[21]  Didier Le Gall,et al.  MPEG: a video compression standard for multimedia applications , 1991, CACM.