Vehicle detection and classification from acoustic signal using ANN and KNN

In this paper, a new efficient method for detection and classification of vehicles from acoustic signal using ANN and KNN is presented. Automatic Identification and classification of vehicles is a very challenging area, which is in contrast to the traditional practice of monitoring the vehicles manually. In this paper, an algorithm has been developed and implemented for classification of vehicles belonging to different classes in a typical of Indian scenario. Automatic identification and classification of vehicles is a challenging problem in traffic planning, in contrast to the traditional practice of monitoring traffic manually. This becomes even more challenging in single|double lane road with heterogeneous traffic, which is typical in Indian scenario. In this work we propose an algorithm for automatic detection and broad classification of vehicles in to three categories namely heavy, medium and light. When a vehicle passes the microphone the recorded acoustic signal shows a peak in energy. The energy contour is smoothed and peaks are automatically located for detection of vehicle sound signal. Mel frequency cepstral coefficients are extracted for detection the regions around detected peaks. The feature vectors are used for training ANN/KNN classifiers. Efficiency of the method is illustrated using test data which contains approximately 160 vehicles belonging to different categories.