Ansatte – Københavns Universitet

An in-depth study of sparse codes on abnormality detection

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Huamin Ren, Hong Pan, Søren Ingvor Olsen, Morten Bornø Jensen, Thomas B. Moeslund

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].

OriginalsprogEngelsk
Titel2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Antal sider7
ForlagIEEE
Publikationsdato2016
Sider66-72
Artikelnummer7738016
ISBN (Elektronisk)978-1-5090-3811-4
DOI
StatusUdgivet - 2016
Begivenhed13th IEEE International Conference on Advanced Video and Signal Based Surveillance - University of Colorado, Colorado Springs, USA
Varighed: 23 aug. 201626 aug. 2016
Konferencens nummer: 13

Konference

Konference13th IEEE International Conference on Advanced Video and Signal Based Surveillance
Nummer13
LokationUniversity of Colorado
LandUSA
ByColorado Springs
Periode23/08/201626/08/2016

ID: 176373739