Vehicle type categorization: A comparison of classification schemes
This paper describes research to classify road vehicles into a range of broad categories using simple measures of size and shape derived from view-dependent binary silhouettes using images derived from a static roadside CCTV camera. A novel approach to camera calibration utilizes calibrated images mapped by Google Earth to provide accurately-surveyed scene geometry that is manually corresponded with visible groundplane landmarks in the CCTV images. In the experiments reported here, manual segmentation is used to delineate vehicles in the images and a set of scaled features is extracted from each binary silhouette. Classification assigns each blob to one of four vehicle classes (car, van, bus and bicycle/motorcycle) using two feature-based classifiers (SVM and random forests) and a model-based approach. Results are presented for 10-fold cross validation study involving over 2000 manually labeled silhouettes. A peak classification performance of 96.26% is observed for SVM.