A Multilevel Banded Graph Cuts Method for Fast Image Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
Real-Time Object Tracking for Augmented Reality Combining Graph Cuts and Optical Flow
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moving object detection in the H.264/AVC compressed domain for video surveillance applications
Journal of Visual Communication and Image Representation
Fast Compressed Domain Motion Detection in H.264 Video Streams for Video Surveillance Applications
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
A Spatiotemporal Motion-Vector Filter for Object Tracking on Compressed Video
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Graph-Based Multiplayer Detection and Tracking in Broadcast Soccer Videos
IEEE Transactions on Multimedia
Overview of the Scalable Video Coding Extension of the H.264/AVC Standard
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Multimedia
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In this paper we present a novel method of detecting and tracking moving objects in H.264/SVC bitstreams for video surveillance applications. Efficient detection and reliable tracking of real moving objects are first performed in the spatial base layer of H.264/SVC based on a spatio-temporal graph which is constructed from the block partitions with non-zero motion vectors and/or non-zero residue information. The spatio-temporal graph is utilized in reliably maintaining the real moving objects of being detected and tracked by removing false detected objects via graph pruning and graph projection. Graph matching is then performed to precisely identify the real moving objects over time even under occlusion. For low-complex but accurate detection and reliable tracking of moving objects in spatial enhancement layer of H.264/SVC, inter-layer graph mapping and intra-layer graph refinement are used without performing graph pruning, graph projection and graph matching which are mostly performed in the spatial base layer. For this, the identified block groups of the real moving objects in the spatial base layer are then mapped to the spatial enhancement layer to provide accurate and efficient object detection and tracking in the bitstreams of higher spatial resolution. Experimental results show the proposed method can reliably detect small objects, object occlusions and object separation. It also produces efficient processing time down to 27% compared to fully performing graph processing in both spatial base and enhancement layers of H.264/SVC test bitstreams.