Automatic event detection of abandoned and stolen objects in real-time surveillance

Weihua Wang; Zhijing Liu

doi:10.1117/12.867039

20 August 2010 Automatic event detection of abandoned and stolen objects in real-time surveillance

Weihua Wang, Zhijing Liu

Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 78202O (2010) https://doi.org/10.1117/12.867039
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China

Abstract

This paper describes an approach aimed at automatic identification of events of abandoned and stolen objects in variety environment. Our approach mainly include three steps of data processing: the first processing phrase is object extraction, involving a dual-time background subtraction algorithm which dynamically updates two sets of background. Then, extracted objects are classified as static or dynamic objects and human or non-human objects. Finally, a decision-making model is employed to calculate a confidence score for the classification about event, and an alarm will be automatically triggered if the certainty of action is higher than a pre-defined threshold. Also, the robustness and efficiency of the method was tested on our real time video surveillance system and evaluated by public database such as AVSS 2007 datasets.

Citation Download Citation

Weihua Wang and Zhijing Liu "Automatic event detection of abandoned and stolen objects in real-time surveillance", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78202O (20 August 2010); https://doi.org/10.1117/12.867039

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