Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/591388
Title: Abnormal Event Detection via Compact Low-Rank Sparse Learning
Authors: Zhong Zhang;Xing Mei;Baihua Xiao
subject: pattern recognition|video analysis|intelligent systems|feature representation
Year: 2016
Publisher: IEEE
Abstract: Sparsity-based methods have been recently applied to abnormal event detection, and have achieved impressive results. However, most such methods fail to consider the relationship among coefficient vectors; furthermore, they neglect the underlying "dictionary structure."' The authors' compact low-rank sparse representation (CLSR) method overcomes these drawbacks. Specifically, it adds compact regularization to the sparse representation model, which explicitly considers the relationship among coefficient vectors. The authors utilize the low-rank property to capture the underlying dictionary structure. Their method is verified on three challenging databases, and the experimental results demonstrate that it compares favorably to state-of-the-art methods in abnormal event detection.
Description: 
URI: http://localhost/handle/Hannan/174006
http://localhost/handle/Hannan/591388
ISSN: 1541-1672
volume: 31
issue: 2
Appears in Collections:2016

Files in This Item:
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Title: Abnormal Event Detection via Compact Low-Rank Sparse Learning
Authors: Zhong Zhang;Xing Mei;Baihua Xiao
subject: pattern recognition|video analysis|intelligent systems|feature representation
Year: 2016
Publisher: IEEE
Abstract: Sparsity-based methods have been recently applied to abnormal event detection, and have achieved impressive results. However, most such methods fail to consider the relationship among coefficient vectors; furthermore, they neglect the underlying "dictionary structure."' The authors' compact low-rank sparse representation (CLSR) method overcomes these drawbacks. Specifically, it adds compact regularization to the sparse representation model, which explicitly considers the relationship among coefficient vectors. The authors utilize the low-rank property to capture the underlying dictionary structure. Their method is verified on three challenging databases, and the experimental results demonstrate that it compares favorably to state-of-the-art methods in abnormal event detection.
Description: 
URI: http://localhost/handle/Hannan/174006
http://localhost/handle/Hannan/591388
ISSN: 1541-1672
volume: 31
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7325203.pdf139.07 kBAdobe PDFThumbnail
Preview File
Title: Abnormal Event Detection via Compact Low-Rank Sparse Learning
Authors: Zhong Zhang;Xing Mei;Baihua Xiao
subject: pattern recognition|video analysis|intelligent systems|feature representation
Year: 2016
Publisher: IEEE
Abstract: Sparsity-based methods have been recently applied to abnormal event detection, and have achieved impressive results. However, most such methods fail to consider the relationship among coefficient vectors; furthermore, they neglect the underlying "dictionary structure."' The authors' compact low-rank sparse representation (CLSR) method overcomes these drawbacks. Specifically, it adds compact regularization to the sparse representation model, which explicitly considers the relationship among coefficient vectors. The authors utilize the low-rank property to capture the underlying dictionary structure. Their method is verified on three challenging databases, and the experimental results demonstrate that it compares favorably to state-of-the-art methods in abnormal event detection.
Description: 
URI: http://localhost/handle/Hannan/174006
http://localhost/handle/Hannan/591388
ISSN: 1541-1672
volume: 31
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7325203.pdf139.07 kBAdobe PDFThumbnail
Preview File