An In-depth Study of Sparse Codes on Abnormality Detection

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

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

3 Citationer (Scopus)
368 Downloads (Pure)


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 abnormal-
ity 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].
TitelIEEE Advanced Video and Signal-based Surveillance (AVSS) 2016
Antal sider7
ISBN (Trykt)978-1-5090-3812-1
ISBN (Elektronisk)978-1-5090-3811-4
StatusUdgivet - 2016
BegivenhedThe 13th International Conference on Advanced Video and Signal-Based Surveillance - Colorado Springs, USA
Varighed: 23 aug. 201626 aug. 2016
Konferencens nummer: 13


KonferenceThe 13th International Conference on Advanced Video and Signal-Based Surveillance
By Colorado Springs

Fingeraftryk Dyk ned i forskningsemnerne om 'An In-depth Study of Sparse Codes on Abnormality Detection'. Sammen danner de et unikt fingeraftryk.