An in-depth study of sparse codes on abnormality detection

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

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. s. 66-72 7738016.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Ren, H, Pan, H, Olsen, SI, Jensen, MB & Moeslund, TB 2016, An in-depth study of sparse codes on abnormality detection. i 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)., 7738016, IEEE, s. 66-72, 13th IEEE International Conference on Advanced Video and Signal Based Surveillance, Colorado Springs, USA, 23/08/2016. https://doi.org/10.1109/AVSS.2016.7738016

APA

Ren, H., Pan, H., Olsen, S. I., Jensen, M. B., & Moeslund, T. B. (2016). An in-depth study of sparse codes on abnormality detection. I 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (s. 66-72). [7738016] IEEE. https://doi.org/10.1109/AVSS.2016.7738016

Vancouver

Ren H, Pan H, Olsen SI, Jensen MB, Moeslund TB. An in-depth study of sparse codes on abnormality detection. I 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE. 2016. s. 66-72. 7738016 https://doi.org/10.1109/AVSS.2016.7738016

Author

Ren, Huamin ; Pan, Hong ; Olsen, Søren Ingvor ; Jensen, Morten Bornø ; Moeslund, Thomas B. / An in-depth study of sparse codes on abnormality detection. 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2016. s. 66-72

Bibtex

@inproceedings{89d6dbd9a6864a4f944d7d2666d5600a,
title = "An in-depth study of sparse codes on abnormality detection",
abstract = "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].",
author = "Huamin Ren and Hong Pan and Olsen, {S{\o}ren Ingvor} and Jensen, {Morten Born{\o}} and Moeslund, {Thomas B.}",
year = "2016",
doi = "10.1109/AVSS.2016.7738016",
language = "English",
pages = "66--72",
booktitle = "2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)",
publisher = "IEEE",

}

RIS

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.

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

SP - 66

EP - 72

BT - 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

PB - IEEE

ER -

ID: 176373739