Video Object Classification System with Shadow Removal using Gaussian Mixture Model
Classification is the process of assigning a class to a group of objects. Moving objects classification can be difficult a task in the presence of dynamic factors like occlusion clutters and shadows. This paper developed a classifier for moving images (video stream) by using a modified adaptive background mixture model method. This system removes shadows and correctly classifies moving objects as human, human group and vehicles.
Background Mixture Model is common technique used in Computer Vision, Video object classification is not an exception, many background models have been designed to address different problems ranging from slow start and shadow removal; this paper presents a method which models each pixel as a mixture of Gaussians and using a Maximum A Posteriori approximation to update the model, this paper also introduces a two level shadow removal technique which suppressed shadows in colour and texture consistencies in the classified objects so that the system will not mis- classify moving shadows as objects. This work overcome the problem of slow learning in busy environment and can classify more than one object in view of the camera
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