Vehicle classification for traffic surveillance videos based on spatial location information and Sparse Representation-based Classifier

Vehicle classification plays an important part in Intelligent Transport System (ITS). However, the existing vehicle classification methods are not very robust to various changes such as lighting, weathers, noises, and the classification accuracy has been requiring to be improved. Sparse Representation-based Classifier (SRC) is not sensitive to the shortage and damage of data, the feature selection methods, is robust to lighting changes of the images, and can achieve excellent performance in multi-class classification problems. Therefore, this paper presents a new method based on spatial location information and SRC. Firstly, taken the specific characteristics of vehicles into consideration, the features of HOG (Histogram of Oriented Gradient) and HU moments are extracted to characterize the property of vehicles. In addition, the spatial location information is added to HU moment features in this paper to improve its ability to distinguish and describe vehicles. Then, vehicles are classified into six classes (large bus, car, motorcycle, minibus, truck and van) by SRC. The performance evaluation is carried out on the dataset which consists of 13300 vehicle images extracted from the real highway surveillance videos. The experimental results show that, using the proposed method, the time cost of classification can be speeded up by about 1.57 times and the average classification precision can achieve 96.53%, which is drastically improved by more than 2.7% compared to other existing methods.