NDLI: Adaptive spatio-temporal background subtraction using improved Wronskian change detection scheme in Gaussian mixture model framework

Content Provider	IET Digital Library
Author	Panda, Deepak Kumar Meher, Sukadev
Abstract	Background subtraction (BS) is a fundamental step for moving object detection in various video surveillance applications. Gaussian mixture model (GMM) is a widely used BS technique which provides a good compromise between robustness to the background variations and real-time constraints. However, GMM does not support the spatial relationship among neighbouring pixels and it uses a fixed learning rate for every pixel during the parameter update. On the other hand, Wronskian change detection model (WM) is a spatial-domain BS technique which solves misclassification of pixels but fails in the presence of dynamic background. In this study, a novel spatio-temporal BS technique is proposed that exploits spatial relation of Wronskian function and employs it with a new fuzzy adaptive learning rate in a GMM framework. Instead of using WM directly, an improved WM is proposed by adaptively finding out the ratio of the current pixel to the background pixel or its reciprocal, and a weighted Wronskian is developed to mitigate the effect of dynamic background pixels. Additionally, a new fuzzy adaptive learning rate is employed in the GMM framework. Experimental results of the proposed framework yield better silhouette of the moving objects as compared with the state-of-the-art techniques.
Starting Page	1832
Ending Page	1843
Page Count	12
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 10, Oct (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0595
Journal	IET Image Processing
Publisher Date	2018-04-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Spatio-temporal Background Subtraction Background Pixel Background Variations Computer Vision And Image Processing Technique Dynamic Background Pixel Fixed Learning Rate Framework Yield Better Silhouette Fundamental Step Fuzzy Adaptive Learning Rate Gaussian Mixture Model Framework Gaussian Processes GMM Framework Image Motion Analysis Image Recognition Image Sequence Improved Wronskian Change Detection Scheme Knowledge Engineering Technique Learning in AI Moving Objects Neighbouring Pixel Object Detection Parameter Update Real-time Constraint Spatial Relationship Spatial-domain BS Technique Spatiotemporal Phenomena Statistics Video Signal Processing Video Surveillance Video Surveillance Application Weighted Wronskian WM Directly Wronskian Change Detection Model Wronskian Function
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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