An in-depth study of sparse codes on abnormality detection
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An in-depth study of sparse codes on abnormality detection. / Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor; Jensen, Morten Bornø; Moeslund, Thomas B.
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2016. p. 66-72 7738016.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - An in-depth study of sparse codes on abnormality detection
AU - Ren, Huamin
AU - Pan, Hong
AU - Olsen, Søren Ingvor
AU - Jensen, Morten Bornø
AU - Moeslund, Thomas B.
N1 - Conference code: 13
PY - 2016
Y1 - 2016
N2 - Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality detection. We present an in-depth study of two types of sparse codes solutions-greedy algorithms and convex L1-norm solutions-and their impact on abnormality detection performance. We also propose our framework of combining sparse codes with different detection methods. Our comparative experiments are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. The experiment results show that combining OMP codes with maximum coordinate detection could achieve state-of-The-Art performance on the UCSD dataset [14].
AB - Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality detection. We present an in-depth study of two types of sparse codes solutions-greedy algorithms and convex L1-norm solutions-and their impact on abnormality detection performance. We also propose our framework of combining sparse codes with different detection methods. Our comparative experiments are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. The experiment results show that combining OMP codes with maximum coordinate detection could achieve state-of-The-Art performance on the UCSD dataset [14].
UR - http://www.scopus.com/inward/record.url?scp=85003977003&partnerID=8YFLogxK
U2 - 10.1109/AVSS.2016.7738016
DO - 10.1109/AVSS.2016.7738016
M3 - Article in proceedings
AN - SCOPUS:85003977003
SP - 66
EP - 72
BT - 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
PB - IEEE
Y2 - 23 August 2016 through 26 August 2016
ER -
ID: 176373739