EDeN: Ensemble of Deep Networks for Vehicle Classification

Abstract

Traffic surveillance has always been a challenging task to automate. The main difficulties arise from the high variation of the vehicles appertaining to the same category, low resolution, changes in illumination and occlusions. Due to the lack of large labeled datasets, deep learning techniques still have not shown their full potential. In this paper, we train an Ensemble of Deep Networks (EDeN) to successfully classify surveillance images into eleven different classes of vehicles. The MIO-TCD dataset consists of 786,702 images with high diversity and resembles a real-world environment. Extensive evaluation was performed using individual networks and different combinations of ensembles. Experimental results show that ensemble of networks gives better performance compared to individual networks and it is robust to noise. The ensemble of networks achieves an accuracy of 97.80%, mean precision of 94.39%, mean recall of 91.90% and Cohen kappa of 96.58%.