Automatic moving object and background separation
Signal Processing - Video segmentation for content-based processing manipulation
Fast and Robust Object Detection Framework in Compressed Domain
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Real-Time Foreground Segmentation for the Moving Camera Based on H.264 Video Coding Information
FGCN '07 Proceedings of the Future Generation Communication and Networking - Volume 01
A Real Time Spatial/Temporal/Motion Integrated Surveillance System in Compressed Domain
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 03
Compressed Domain Motion Analysis for Video Semantic Events Detection
ICIE '09 Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 01
Fast scene change detection using direct feature extraction fromMPEG compressed videos
IEEE Transactions on Multimedia
Detection of moving objects in video using a robust motion similarity measure
IEEE Transactions on Image Processing
Quantifying motion in video recordings of neonatal seizures by regularized optical flow methods
IEEE Transactions on Image Processing
Compressed Domain Video Object Segmentation
IEEE Transactions on Circuits and Systems for Video Technology
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Video streaming is characterized by a deep heterogeneity due to the availability of many different video standards such as H.262, H.263, MPEG-4/H.264, H.261 and others. In this situation two approaches to motion segmentation are possible: the first needs to decode each stream before processing it, with a high computational complexity, while the second is based on video processing in the coded domain, with the disadvantage of coupling between implementation and the coded stream. In this paper a motion segmentation based on a "generic encoded video model" is proposed. It aims at building applications in the encoded domain independently by target codec. This can be done by a video stream representation based on a semantic abstraction of the video syntax. This model joins the advantages of the two previous approaches by making it possible working in real time, with low complexity, and with small latency. The effectiveness of the proposed representation is evaluated on a low complexity video segmentation of moving objects.