Unsupervised soccer video abstraction based on pitch, dominant color and camera motion analysis
Proceedings of the 12th annual ACM international conference on Multimedia
Motion-Based Selection of Relevant Video Segments for Video Summarization
Multimedia Tools and Applications
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Multimodal semantic analysis and annotation for basketball video
EURASIP Journal on Applied Signal Processing
Structure and event mining in sports video with efficient mosaic
Multimedia Tools and Applications
Generalized least squares-based parametric motion estimation
Computer Vision and Image Understanding
Parametric model-based motion segmentation using surface selection criterion
Computer Vision and Image Understanding
Adaptive robust estimation of affine parameters from block motion vectors
Image and Vision Computing
ACT '10 Proceedings of the 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies
Robust motion-compensated video upconversion
IEEE Transactions on Consumer Electronics
Rapid estimation of camera motion from compressed video with application to video annotation
IEEE Transactions on Circuits and Systems for Video Technology
Global motion estimation from coarsely sampled motion vector field and the applications
IEEE Transactions on Circuits and Systems for Video Technology
Robust Dominant Motion Estimation Using MPEG Information in Sport Sequences
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose a novel block-based approach for global motion estimation (GME) and panorama image construction in encoded MPEG-1 video streams. Direct extraction of motion vectors (MV) from MPEG stream greatly improves efficiency of our method against pixel-based methods. However, some MVs in MPEG stream do not indicate real object or camera motion in the scene. Therefore, we introduce a reliability measure to discriminate real MVs from noisy or outlier MVs. In addition, an iterative reweighting process is applied to increase accuracy of GME. Finally, panorama image of several sequences is constructed by using estimated camera motion. Experiments show that the proposed method has high accuracy and can produce high quality panorama images for compressed videos faster than realtime.