X-splines: a spline model designed for the end-user
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Rapid Object Tracking on Compressed Video
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Motion segmentation and pose recognition with motion history gradients
Machine Vision and Applications - Special issue: IEEE WACV
MPEG-2 compressed-domain algorithms for video analysis
EURASIP Journal on Applied Signal Processing
Lightweight object tracking in compressed video streams demonstrated in region-of-interest coding
EURASIP Journal on Applied Signal Processing
Multiple moving object detection for fast video content description in compressed domain
EURASIP Journal on Advances in Signal Processing
Moving object tracking in H.264/AVC bitstream
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Shot boundary detection for H.264/AVC bitstreams with frames containing multiple types of slices
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Object tracking using background subtraction and motion estimation in MPEG videos
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
A unified approach to shot change detection and camera motion characterization
IEEE Transactions on Circuits and Systems for Video Technology
Object tracking for retrieval applications in MPEG-2
IEEE Transactions on Circuits and Systems for Video Technology
Video object segmentation: a compressed domain approach
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Moving object segmentation in the h.264 compressed domain
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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This paper presents a simple and fast method for unsupervised trajectory estimation of multiple moving objects within a video scene. It is entirely based on the motion vectors that are present in compressed H.264/AVC or SVC video streams. We extract these motion vectors, perform robust frame-wise global motion estimation and use these estimates to form outlier masks. Motion segmentation on the spatio-temporally filtered outlier masks is performed to detect moving regions in the scene, which are analyzed over time in order to identify similar objects in adjacent frames. The construction of so-called Object History Images (OHIs) is proposed to stabilize the trajectories, which are finally interpolated with X-splines. The system enables real-time analysis with standard hardware.