Regular Articles

Detecting anomalies in crowded scenes via locality-constrained affine subspace coding

[+] Author Affiliations
Yaxiang Fan, Gongjian Wen, Shaohua Qiu

National University of Defense Technology, Science and Technology on Automatic Target Recognition Laboratory, Changsha, China

Deren Li

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan, China

J. Electron. Imaging. 26(4), 043002 (Jul 06, 2017). doi:10.1117/1.JEI.26.4.043002
History: Received February 11, 2017; Accepted June 15, 2017
Text Size: A A A

Abstract.  Video anomaly event detection is the process of finding an abnormal event deviation compared with the majority of normal or usual events. The main challenges are the high structure redundancy and the dynamic changes in the scenes that are in surveillance videos. To address these problems, we present a framework for anomaly detection and localization in videos that is based on locality-constrained affine subspace coding (LASC) and a model updating procedure. In our algorithm, LASC attempts to reconstruct the test sample by its top-k nearest subspaces, which are obtained by segmenting the normal samples space using a clustering method. A sample with a large reconstruction cost is detected as abnormal by setting a threshold. To adapt to the scene changes over time, a model updating strategy is proposed. We experiment on two public datasets: the UCSD dataset and the Avenue dataset. The results demonstrate that our method achieves competitive performance at a 700 fps on a single desktop PC.

Figures in this Article
© 2017 SPIE and IS&T

Citation

Yaxiang Fan ; Gongjian Wen ; Shaohua Qiu and Deren Li
"Detecting anomalies in crowded scenes via locality-constrained affine subspace coding", J. Electron. Imaging. 26(4), 043002 (Jul 06, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.4.043002


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement


 

  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.