DocumentCode :
3283440
Title :
Extract foreground objects based on sparse model of spatiotemporal spectrum
Author :
Zhangjian Ji ; Weiqiang Wang ; Ke Lu
Author_Institution :
Sch. of Comput. & Control Eng., Univ. of Chinese Acad. of Sci., Beijing, China
fYear :
2013
fDate :
15-18 Sept. 2013
Firstpage :
3441
Lastpage :
3445
Abstract :
In this paper, we present a novel foreground object detection method based on the sparse model of the spectrum of spatiotemporal DCT domain, which is robust for high dynamic scenes. First, we adopt the three-dimensional Discrete Cosine Transform (DCT) to calculate the spatiotemporal spectrum representation of the current frame. Then, identification of foreground pixels is formulated as the analysis of the sparse solution of an optimization problem, where foreground pixels correspond to an outlier of the sparse model. Finally, the background updating method is presented to adaptively update the dictionary of sparse model corresponding to background representation. The experimental results on four challenging video sequences show that the proposed method is more robust to high dynamic changes of scenes compared with four representative methods.
Keywords :
discrete cosine transforms; feature extraction; image representation; image sequences; object detection; optimisation; background representation; background updating method; foreground object detection method; foreground objects extraction; optimization problem; sparse model; sparse model dictionary; sparse solution; spatiotemporal DCT domain; spatiotemporal spectrum representation; three-dimensional discrete cosine transform; video sequences; Sparse model; Spatiotemporal spectrum; foreground object detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2013 20th IEEE International Conference on
Conference_Location :
Melbourne, VIC
Type :
conf
DOI :
10.1109/ICIP.2013.6738710
Filename :
6738710
Link To Document :
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