Robust event detection scheme for complex scenes in video surveillance

Erkang Chen; Yi Xu; Xiaokang Yang; Wenjun Zhang

doi:10.1117/1.3596603

1 July 2011 Robust event detection scheme for complex scenes in video surveillance

Erkang Chen, Yi Xu, Xiaokang Yang, Wenjun Zhang

Author Affiliations +

Funded by: Doctoral Program of Higher Education of China, National Natural Science Foundation of China, NSFC, National Basic Research Program of China, 973 Program

Optical Engineering, Vol. 50, Issue 7, 077204 (July 2011). https://doi.org/10.1117/1.3596603

Abstract

Event detection for video surveillance is a difficult task due to many challenges: cluttered background, illumination variations, scale variations, occlusions among people, etc. We propose an effective and efficient event detection scheme in such complex situations. Moving shadows due to illumination are tackled with a segmentation method with shadow detection, and scale variations are taken care of using the CamShift guided particle filter tracking algorithm. For event modeling, hidden Markov models are employed. The proposed scheme also reduces the overall computational cost by combing two human detection algorithms and using tracking information to aid human detection. Experimental results on TRECVid event detection evaluation demonstrate the efficacy of the proposed scheme. It is robust, especially to moving shadows and scale variations. Employing the scheme, we achieved the best run results for two events in the TRECVid benchmarking evaluation.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Erkang Chen, Yi Xu, Xiaokang Yang, and Wenjun Zhang "Robust event detection scheme for complex scenes in video surveillance," Optical Engineering 50(7), 077204 (1 July 2011). https://doi.org/10.1117/1.3596603

Published: 1 July 2011

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