Caste Shadow Removal in Vehicle Detection using Mixture of Gaussian for Traffic Surveillance System
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Abstract
Observing moving objects in a site and removal of caste shadow is a critical task in computer vision. This paper presents an algorithm for
detection and caste removal of vehicles in real-time video which is streamed by a camera with fixed position. Processing of the video is done in
two steps: Vehicle Detection and Caste shadow removal. Identifying moving object is done by classification of pixels into either foreground
(object) or background. Vehicle detection is achieved by the use of Background subtraction. Many existing scheme of background removal
presenting different background models like Mixture of Gaussian (MoG) and Joint Random Field (JRF) will be discussed. A simple approach to
remove caste shadow area from the detected foreground objects will also be discussed.
Keywords: Background subtraction; Gaussian Mixture Model; JRF model; Caste Shadow; Thresolding
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