Introduction to Algorithms
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Bayesian method for motion segmentation and tracking in compressed videos
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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
Tracking visible boundary of objects using occlusion adaptive motion snake
IEEE Transactions on Image Processing
Video object segmentation: a compressed domain approach
IEEE Transactions on Circuits and Systems for Video Technology
An Approach to Trajectory Estimation of Moving Objects in the H.264 Compressed Domain
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Compressed domain indexing of scalable H.264/SVC streams
Image Communication
Multi-view Object Localization in H.264/AVC Compressed Domain
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Estimating motion reliability to improve moving object detection in the H.264/AVC domain
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Efficient region-of-interest scalable video coding with adaptive bit-rate control
Advances in Multimedia
Hi-index | 0.00 |
Data broadcasting services are required to provide user interactivity through connecting additional contents such as object information to audio-visual contents. H.264/AVC-based metadata authoring tools include functions which identify and track position and motion of objects. In this work, we propose a method for tracking the target object by using partially decoded texture data and motion vectors extracted directly from H.264/AVC bitstream. This method achieves low computational complexity and high performance through the dissimilarity energy minimization algorithm which tracks feature points adaptively according to these characteristics. The experiment has shown that the proposed method had high performance with fast processing time.