We proposed 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.
This work introduces a framework for video summarisation and browsing by utilising inherently hierarchical compressed-domain features of scalable video and efficient dynamic video summarisation. This approach enables instant adaptability of generated video summaries to available channel bandwidth as well as display resources. By utilising compressed domain features an efficient hierarchical analysis of motion activity at different layers of complexity is achieved. Exploiting a contour evolution algorithm, a scale space of temporal video descriptors is generated, enabling rapid video summarisation. Given the spatial resources of the terminal display and generated video summary, the final browsing layout is generated utilising an unsupervised robust spectral clustering technique and a fast discrete optimisation algorithm. Results show excellent scalability of the video summaries and good algorithm efficiency.