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Dealing Background Issues in Object Detection using GMM: A Survey

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Lajari Alandkar, Sachin R. Gengaje

Lajari Alandkar and Sachin R Gengaje. Dealing Background Issues in Object Detection using GMM: A Survey. International Journal of Computer Applications 150(5):50-55, September 2016. BibTeX

	author = {Lajari Alandkar and Sachin R. Gengaje},
	title = {Dealing Background Issues in Object Detection using GMM: A Survey},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2016},
	volume = {150},
	number = {5},
	month = {Sep},
	year = {2016},
	issn = {0975-8887},
	pages = {50-55},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2016911508},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Moving object detection is critical task in video analytics. Gaussian Mixture Model (GMM) based background subtraction is widely popular technique for moving object detection due to its robustness to multimodality and lighting changes. This paper presents the critical survey about various GMM based approaches for handling critical background situations. This survey describes various challenges faced by background subtraction such as shadow, sudden and slow light changes, multimodal background, bootstrap, camouflage, foreground aperture, camera jitter etc. and study of various modifications or extensions of GMM to handle these issues. This study helps researcher to select appropriate GMM version based on critical background condition.


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Object Detection, Background Subtraction, Gaussian Mixture Model, Background challenges