NDLI: High variation removal for background subtraction in traffic surveillance systems

Content Provider	IET Digital Library
Author	Ha, Synh Viet Uyen Tran, Duong Nguyen Ngoc Nguyen, Tien Phuoc Dao, Son Vu Truong
Abstract	Background subtraction has been a fundamental task in video analytics and smart surveillance applications. In the field of background subtraction, Gaussian mixture model is a canonical model for many other methods. However, the unconscious learning of this model often leads to erroneous motion detection under high variation scenes. This article proposes a new method that incorporates entropy estimation and a removal framework into the Gaussian mixture model to improve the performance of background subtraction. Firstly, entropy information is computed for each pixel of a frame to classify frames into silent or high variation categories. Secondly, the removal framework is used to determine which frames from the background subtraction process are updated. The proposed method produces precise results with fast execution time, which are two critical factors in surveillance systems for more advanced tasks. The authors used two publicly available test sequences from the 2014 Change Detection and Scene background modelling data sets and internally collected data sets of scenes with dense traffic.
Starting Page	1163
Ending Page	1170
Page Count	8
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 8, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5033
Journal	IET Computer Vision
Publisher Date	2018-07-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Background Subtraction Process Computer Vision And Image Processing Technique Entropy Entropy Estimation Entropy Information Erroneous Motion Detection Fast Execution Time Frame Classification Gaussian Mixture Model Gaussian Processes High Variation Removal Image Classification Image Motion Analysis Image Recognition Mixture Model Object Detection Smart Surveillance Application Statistics Traffic Engineering Computing Traffic Surveillance System Video Analytics Video Signal Processing Video Surveillance
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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