Abnormal event detection in crowded scenes using two sparse dictionaries with saliency

Yaping Yu; Wei Shen; He Huang; Zhijiang Zhang

doi:10.1117/1.JEI.26.3.033013

22 May 2017 Abnormal event detection in crowded scenes using two sparse dictionaries with saliency

Yaping Yu, Wei Shen, He Huang, Zhijiang Zhang

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 3, 033013 (May 2017). https://doi.org/10.1117/1.JEI.26.3.033013

Abstract

Abnormal event detection in crowded scenes is a challenging problem due to the high density of the crowds and the occlusions between individuals. We propose a method using two sparse dictionaries with saliency to detect abnormal events in crowded scenes. By combining a multiscale histogram of optical flow (MHOF) and a multiscale histogram of oriented gradient (MHOG) into a multiscale histogram of optical flow and gradient, we are able to represent the feature of a spatial–temporal cuboid without separating the individuals in the crowd. While MHOF captures the temporal information, MHOG encodes both spatial and temporal information. The combination of these two features is able to represent the cuboid’s appearance and motion characteristics even when the density of the crowds becomes high. An abnormal dictionary is added to the traditional sparse model with only a normal dictionary included. In addition, the saliency of the testing sample is combined with two sparse reconstruction costs on the normal and abnormal dictionary to measure the normalness of the testing sample. The experiment results show the effectiveness of our method.

Citation Download Citation

Yaping Yu, Wei Shen, He Huang, and Zhijiang Zhang "Abnormal event detection in crowded scenes using two sparse dictionaries with saliency," Journal of Electronic Imaging 26(3), 033013 (22 May 2017). https://doi.org/10.1117/1.JEI.26.3.033013

Received: 15 December 2016; Accepted: 3 May 2017; Published: 22 May 2017

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available