Moving object segmentation algorithm based on cellular neural networks in the H.264 compressed domain

2009 ◽  
Vol 48 (7) ◽  
pp. 077001 ◽  
Author(s):  
Jie Feng
2009 ◽  
Vol 09 (04) ◽  
pp. 609-627 ◽  
Author(s):  
J. WANG ◽  
N. V. PATEL ◽  
W. I. GROSKY ◽  
F. FOTOUHI

In this paper, we address the problem of camera and object motion detection in the compressed domain. The estimation of camera motion and the moving object segmentation have been widely stated in a variety of context for video analysis, due to their capabilities of providing essential clues for interpreting the high-level semantics of video sequences. A novel compressed domain motion estimation and segmentation scheme is presented and applied in this paper. MPEG-2 compressed domain information, namely Motion Vectors (MV) and Discrete Cosine Transform (DCT) coefficients, is filtered and manipulated to obtain a dense and reliable Motion Vector Field (MVF) over consecutive frames. An iterative segmentation scheme based upon the generalized affine transformation model is exploited to effect the global camera motion detection. The foreground spatiotemporal objects are separated from the background using the temporal consistency check to the output of the iterative segmentation. This consistency check process can coalesce the resulting foreground blocks and weed out unqualified blocks. Illustrative examples are provided to demonstrate the efficacy of the proposed approach.


2012 ◽  
Vol 41 (8) ◽  
pp. 914-921
Author(s):  
王慧斌 WANG Hui-bin ◽  
沈俊雷 SHEN Jun-lei ◽  
王鑫 WANG Xin ◽  
张丽丽 ZHANG Li-li

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