Statistical model-based change detection in moving video
Signal Processing
Digital modulation and coding
Multiple video object tracking in complex scenes
Proceedings of the tenth ACM international conference on Multimedia
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video Mosaics for Virtual Environments
IEEE Computer Graphics and Applications
Automatic measurement of quality metrics for colonoscopy videos
Proceedings of the 13th annual ACM international conference on Multimedia
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This paper presents an algorithm for automatically extracting significant motion trajectories in sports videos. Our approach consists of four stages: global motion estimation, motion blob detection, trajectory evolution and trajectory refinement. Global motion is estimated from the motion vectors in the compressed video using an iterative algorithm with robust outlier rejection. A statistical hypothesis test is carried out within the Block Rejection Map(BRM), which is the by-product of the global motion estimation, for the detection of motion blobs. Trajectory evolution is the process in which the motion blobs are either appended to an existing trajectory or are considered to be the beginning of a new trajectory based on its distance to an adaptive trajectory description. Finally, the extracted motion trajectories are refined using a Kalman filter. Experimental results on both indoor and outdoor sports videos demonstrate the effectiveness and efficiency of the proposed method.