In order to optimize the weights of the network, greedy layer wise pre-training approach is used initially and the fine tuning of the network is done using conjugate gradient based back propagation algorithm. The background model generated herein is obtained by training the incoming frames of the surveillance video with the deep learning network in an unsupervised manner. In this paper, an efficient deep learning technique based on autoencoder network is used for modeling the background. Moreover, few recently proposed efficient approaches are not validated on the basis of some of the challenging applications in which they may fail in its efficiency when tested. However, the need for real time artificial intelligent based low cost approach still exists.
Numerous approaches have been proposed for the same over the past few decades. Background modeling is a major prerequisite for a variety of multimedia applications like video surveillance, traffic monitoring, etc.
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