A Growing Neural Gas Approach to Classify Vehicles in Traffic Environments

Automated video surveillance presents a great amount of applications and one of them is traffic monitoring. Vehicle type detection can provide information about the characteristics of the traffic flow to human traffic controllers in order to facilitate their decision-making process. A video surveillance system is proposed in this work to execute such classification. First of all, a foreground detection and tracking object process has been carried out. Once the vehicles are detected, a feature extraction method obtains the most significant features of this detected vehicles. When the extraction process is done, the vehicle types are determined by employing a set of Growing Neural Gas neural networks. The performance of the proposal has been analyzed from a qualitative and quantitative point of view by using a set of benchmark traffic video sequences, with acceptable results.