Dynamic background modeling using deep learning autoencoder network

Background modeling is a major prerequisite for a variety of multimedia applications like video surveillance, traffic monitoring, etc. Numerous approaches have been proposed for the same over the past few decades. However, the need for real time artificial intelligent based low cost approach still exists. 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. In this paper, an efficient deep learning technique based on autoencoder network is used for modeling the background. 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 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 performance of the algorithm is validated based on the application of unattended object detection in a dynamic environment. Comprehensive assessment of the proposed method using CDNET 2014 dataset and other datasets demonstrates the efficiency of the technique in background modeling.