Video Surveillance for Vehicle Classification using Hybrid SVM and Enhanced Ensemble Classifier

Abstract

Video surveillance has obtained significant growth in the application oriented research fields especially in monitoring and security systems. This paper focuses on utilizing video surveillance in roadside traffic areas for identifying the vehicles such as ambulances in order to provide better road safety especially during emergency situations. The analyses of these situations are done using the video cameras fixed at roadsides for the surveillance purposes with a distributed computing process. These video cameras provide larger video files which are to be processed using efficient machine learning approaches for enhanced vehicle tracking process. In this paper, a road traffic video surveillance system is proposed for automatically identifying the road accidents from the live video files. The proposed system utilizes Hadoop-Map Reduce framework for processing the video files distributive and also in parallel. The distributed video files are enhanced using Gaussian filtering. The background subtraction is carried out Markov Random field (MRF) with Bayesian estimation process along with shadow removal and mist removal for environmental disturbances. The scene classification is carried out using Linear Discriminant Analysis (LDA) while a Hybrid Support Vector Machine (HSVM) combining Kalman filter with the SVM is employed for tracking the vehicles. Thus the proposed method efficiently track and classify the foreground objects with use of object classification and detection in traffic scenes especially in identifying the ambulances and heavy vehicles. The results obtained from the experiments on the proposed research shows the efficiency of traffic monitoring using traffic scenes.