Open AccessHigh variation removal for background subtraction in traffic surveillance systems
Author(s) -
VietUyen Ha Synh,
NguyenNgoc Tran Duong,
Nguyen Tien Phuoc,
VuTruong Dao Son
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5033
Subject(s) - background subtraction , computer science , artificial intelligence , foreground detection , mixture model , gaussian process , computer vision , entropy (arrow of time) , subtraction , pixel , frame (networking) , pattern recognition (psychology) , gaussian , mathematics , telecommunications , physics , arithmetic , quantum mechanics
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.