Dealing Background Issues in Object Detection using GMM: A Survey
نویسندگان
چکیده
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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