Scene Segmentation from Visual Motion Using Global Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVEPS - a compressed video editing and parsing system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
A Unified Approach to Moving Object Detection in 2D and 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic moving object and background separation
Signal Processing - Video segmentation for content-based processing manipulation
A Noise Robust Method for Segmentation of Moving Objects in Video Sequences
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Automatic segmentation of moving objects for video object plane generation
IEEE Transactions on Circuits and Systems for Video Technology
An integrated approach for content-based video object segmentation and retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Foreground detection based on motion vector from compressed video
ICAIT '08 Proceedings of the 2008 International Conference on Advanced Infocomm Technology
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In this paper, we present an effective algorithm for foreground objects segmentation for sports video. This algorithm consists of three steps: low-level features extraction, camera motion estimate, and foreground object extraction. We employ a robust M-estimator to motion vectors fields to estimate global camera motion parameters based on a four-parameter camera motion model, followed by outliers analysis using robust weights instead of the residuals to extract foreground objects. Based on the fact that foreground objects' motion patterns are independent of the global motion model caused by camera motions such as pan, tilt, and zooming, we considers those macro-blocks as foreground, which corresponds to the outliers blocks during robust regression procedure. Experiments showed that the proposed algorithm can robustly extract foreground objects like tennis players and estimate camera motion parameters. Based on these results, high-level semantic video indexing such as event detection and sports video structure analysis can be greatly facilitated. Furthermore, basing the algorithm on compressed domain features can achieve great saving in computation.