The Holy Grail of Content-Based Media Analysis
IEEE MultiMedia
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A mid-level representation framework for semantic sports video analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Trajectory based event tactics analysis in broadcast sports video
Proceedings of the 15th international conference on Multimedia
Video summarisation: A conceptual framework and survey of the state of the art
Journal of Visual Communication and Image Representation
Journal of Visual Communication and Image Representation
Efficient video coding based on audio-visual focus of attention
Journal of Visual Communication and Image Representation
Trajectory-Based Ball Detection and Tracking in Broadcast Soccer Video
IEEE Transactions on Multimedia
Real-Time Motion Trajectory-Based Indexing and Retrieval of Video Sequences
IEEE Transactions on Multimedia
Motion Flow-Based Video Retrieval
IEEE Transactions on Multimedia
Human Behavior Analysis for Highlight Ranking in Broadcast Racket Sports Video
IEEE Transactions on Multimedia
Robust Camera Calibration and Player Tracking in Broadcast Basketball Video
IEEE Transactions on Multimedia
Motion-based video retrieval by trajectory matching
IEEE Transactions on Circuits and Systems for Video Technology
Broadcast Court-Net Sports Video Analysis Using Fast 3-D Camera Modeling
IEEE Transactions on Circuits and Systems for Video Technology
Multimedia Tools and Applications
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This paper presents an approach to recognize jump patterns in human moving trajectory, differentiating jump tracks from planar moving tracks. Since human moving trajectory is one of the most informative representations for content understanding and event detection, trajectory-based video analysis has been gaining popularity. However, a jump action typically leads to violent change in human moving trajectory, since the person suddenly leaves the original plane on which he/she has been moving. The abnormal tracks of the trajectory would influence the performance of trajectory-based video analysis. Hence, differentiating jump tracks from planar moving tracks is of vital importance, not to mention that jump actions typically imply significant events, especially in sports games. In this paper, volleyball videos are used as case study to demonstrate the effectiveness of our proposed jump pattern recognition approach. We derive player trajectory by head tracking, analyze the movement of each player, and recognize potential jump tracks in player trajectories based on two important characteristics: (1) jumps cause pulse-like tracks in the trajectory and (2) the extensions of such tracks go through the vanishing point of vertical lines in the scenes. Finally, the jump positions/heights are estimated, in addition to the planar moving trajectory of each player on the court ground. The experiments show that satisfactory results can be obtained with the proposed recognition scheme.