A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Feature Tracking for Mobile Augmented Reality Using Video Coder Motion Vectors
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Object tracking for retrieval applications in MPEG-2
IEEE Transactions on Circuits and Systems for Video Technology
Semantic video analysis for adaptive content delivery and automatic description
IEEE Transactions on Circuits and Systems for Video Technology
Overview of the Scalable Video Coding Extension of the H.264/AVC Standard
IEEE Transactions on Circuits and Systems for Video Technology
Adaptive Multifeature Tracking in a Particle Filtering Framework
IEEE Transactions on Circuits and Systems for Video Technology
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Recent developments in the video coding technology brought new possibilities of utilising inherently embedded features of the encoded bit-stream in applications such as video adaptation and analysis. Due to the proliferation of surveillance videos there is a strong demand for highly efficient and reliable algorithms for object tracking. This paper presents a new approach for the fast compressed domain analysis utilising motion data from the encoded bit-streams in order to achieve low-processing complexity of object tracking in the surveillance videos. The algorithm estimates the trajectory of video objects by using compressed domain motion vectors extracted directly from standard H.264/MPEG-4 Advanced Video Coding (AVC) and Scalable Video Coding (SVC) bit-streams. The experimental results show comparable tracking precision when evaluated against the standard algorithms in uncompressed domain, while maintaining low computational complexity and fast processing time, thus making the algorithm suitable for real time and streaming applications where good estimates of object trajectories have to be computed fast.