Gaussian Mixture Model and Deep Neural Network based Vehicle Detection and Classification

Abstract

The exponential rise in the demand of vision based traffic surveillance systems have motivated academia-industries to develop optimal vehicle detection and classification scheme. In this paper, an adaptive learning rate based Gaussian mixture model (GMM) algorithm has been developed for background subtraction of multilane traffic data. Here, vehicle rear information and road dash-markings have been used for vehicle detection. Performing background subtraction, connected component analysis has been applied to retrieve vehicle region. A multilayered AlexNet deep neural network (DNN) has been applied to extract higher layer features. Furthermore, scale invariant feature transform (SIFT) based vehicle feature extraction has been performed. The extracted 4096-dimensional features have been processed for dimensional reduction using principle component analysis (PCA) and linear discriminant analysis (LDA). The features have been mapped for SVM-based classification. The classification results have exhibited that AlexNet-FC6 features with LDA give the accuracy of 97.80%, followed by AlexNet-FC6 with PCA (96.75%). AlexNet-FC7 feature with LDA and PCA algorithms has exhibited classification accuracy of 91.40% and 96.30%, respectively. On the contrary, SIFT features with LDA algorithm has exhibited 96.46% classification accuracy. The results revealed that enhanced GMM with AlexNet DNN at FC6 and FC7 can be significant for optimal vehicle detection and classification.