Automated Vehicle detection and classification is an important component of intelligent transport system. Due to significant importance in various fields such as traffic accidents avoidance, toll collection, congestion avoidance, terrorist activities monitoring, security and surveillance systems, intelligent transport system has become important field of study. Various technologies have been used for detecting and classifying vehicles automatically. Automated vehicle detection is broadly divided into two types- Hardware based and software based detection. Various algorithms have been implemented to classify different vehicles from videos. In this paper an efficient and economical solution for automatic vehicle detection and classification is proposed. The proposed system first isolates the object through background subtraction followed by vehicle detection using ontology. Vehicle detection is based on low level features such as shape, size, and spatial location. Finally system classifies vehicles into one of the known classes of vehicle based on size.

Vehicle classification has emerged as a significant field of study because of its importance in variety of applications like surveillance, security system, traffic congestion avoidance and accidents prevention etc. So far numerous algorithms have been implemented for classifying vehicle. Each algorithm follows different procedures for detecting vehicles from videos. By evaluating some of the commonly used techniques we highlighted most beneficial methodology for classifying vehicles. In this paper we pointed out the working of several video based vehicle classification algorithms and compare these algorithms on the basis of different performance metrics such as classifiers, classification methodology or principles and vehicle detection ratio etc. After comparing these parameters we concluded that Hybrid Dynamic Bayesian Network (HDBN) Classification algorithm is far better than the other algorithms due to its nature of estimating the simplest features of vehicles from different videos. HDBN detects vehicles by following important stages of feature extraction, selection and classification. It extracts the rear view information of vehicles rather than other information such as distance between the wheels and height of wheel etc.