Vehicle make and model recognition using random forest classification for intelligent transportation systems

Intelligent Transportation System (ITS) has many real world applications. ITS applications help in better traffic management and enable us to make more secure, intelligent, smart decisions regarding traffic networks. Automatic Surveillance and monitoring is required for many ITS applications. Vehicle Make and Model Recognition (VMMR) can be used to recognize the vehicles' identity. We have designed a Random Forest based VMMR system in this work to identify the Make and Model of a vehicle. The proposed VMMR system is evaluated using a publicly available dataset. Scale Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG) are used for the representation of vehicle images. The proposed method identifies the vehicles with good recognition rates and results are discussed in this paper.