DocumentCode :
1701635
Title :
Moving Object Extraction Using Compressed Domain Features of H.264 INTRA Frames
Author :
Wang, Fu-Ping ; Chung, Wei-Ho ; Ni, Guo-Kai ; Chen, Ing-Yi ; Kuo, Sy-Yen
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
fYear :
2012
Firstpage :
258
Lastpage :
263
Abstract :
A new efficient algorithm using the compressed domain features of H.264 INTRA frames is proposed for moving object extraction on huge video surveillance archives. To achieve searching efficiency, we propose to locate moving objects by scrutinizing only the INTRA frames in video surveillance archives in H.264 compressed domain with short GOP length. In the proposed structure, a modified codebook algorithm is designed to build the block-based background models from the INTRA coding features. Through the subtraction with the background codebook models, the foreground energy frame is filtered and normalized for detecting the existence of moving objects. To overcome the over-segmentation problem and enable the unsupervised searching, a new structure of hysteresis thresholding, where the thresholds are obtained automatically by an efficient algorithm, is adopted to extract foreground blocks. At the final step, the connected components labeling (CCL) and morphological filters are employed to obtain the list of moving objects. As shown in the experimental results, the proposed algorithm outperforms representative existing works.
Keywords :
data compression; feature extraction; filtering theory; image segmentation; video coding; video surveillance; CCL; H.264 INTRA frames; INTRA coding features; background codebook models; block-based background models; codebook algorithm; compressed domain features; connected components labeling; foreground energy frame; hysteresis thresholding; morphological filters; moving object extraction; moving object location; over-segmentation problem; searching efficiency; short GOP length; unsupervised searching; video surveillance archives; Encoding; Feature extraction; Hysteresis; Iron; Training; Vectors; Video surveillance; H.264; codebook background modeling; compressed domain; foreground segmentation; unsupervised analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Video and Signal-Based Surveillance (AVSS), 2012 IEEE Ninth International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-2499-1
Type :
conf
DOI :
10.1109/AVSS.2012.46
Filename :
6328026
Link To Document :
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