The Library
Video anomaly detection with compact feature sets for online performance
Tools
Leyva, Roberto, Sanchez Silva, Victor and Li, Chang-Tsun (2017) Video anomaly detection with compact feature sets for online performance. IEEE Transactions on Image Processing, 26 (7). pp. 3463-3478. doi:10.1109/TIP.2017.2695105 ISSN 1057-7149.
|
PDF
WRAP-video-anomaly-detection-compact-feature-sets-online-performance-Li-2019.pdf - Accepted Version - Requires a PDF viewer. Download (9Mb) | Preview |
Official URL: https://doi.org/10.1109/TIP.2017.2695105
Abstract
Over the past decade, video anomaly detection has been explored with remarkable results. However, research on methodologies suitable for online performance is still very limited. In this paper, we present an online framework for video anomaly detection. The key aspect of our framework is a compact set of highly descriptive features, which is extracted from a novel cell structure that helps to define support regions in a coarse-to-fine fashion. Based on the scene's activity, only a limited number of support regions are processed, thus limiting the size of the feature set. Specifically, we use foreground occupancy and optical flow features. The framework uses an inference mechanism that evaluates the compact feature set via Gaussian Mixture Models, Markov Chains, and Bag-of-Words in order to detect abnormal events. Our framework also considers the joint response of the models in the local spatio-temporal neighborhood to increase detection accuracy. We test our framework on popular existing data sets and on a new data set comprising a wide variety of realistic videos captured by surveillance cameras. This particular data set includes surveillance videos depicting criminal activities, car accidents, and other dangerous situations. Evaluation results show that our framework outperforms other online methods and attains a very competitive detection performance compared with state-of-the-art non-online methods.
Item Type: | Journal Article | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | |||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Video surveillance | |||||||||
Journal or Publication Title: | IEEE Transactions on Image Processing | |||||||||
Publisher: | IEEE | |||||||||
ISSN: | 1057-7149 | |||||||||
Official Date: | July 2017 | |||||||||
Dates: |
|
|||||||||
Volume: | 26 | |||||||||
Number: | 7 | |||||||||
Page Range: | pp. 3463-3478 | |||||||||
DOI: | 10.1109/TIP.2017.2695105 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 22 May 2019 | |||||||||
Date of first compliant Open Access: | 22 May 2019 | |||||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year