Vehicle classification for large-scale traffic surveillance videos using Convolutional Neural Networks

Abstract

Vehicle classification plays an important role in intelligent transport system. However, because the conventional vehicle classification methods are not robust to variations such as illumination, weather, noise, and the classification accuracy cannot meet the requirements of practical applications. Therefore, a new vehicle classification method using Convolutional Neural Networks is proposed in this paper, which consists of two steps: pre-training and fine-tuning. In pre-training, GoogLeNet is pre-trained on ILSVRC-2012 dataset to obtain the initial model with the corresponding connection weights. In fine-tuning, the initial model is further fine-tuned on VehicleDataset which is constructed with 13,700 images in this paper to obtain the final classification model. All images in the VehicleDataset are extracted from real highway surveillance videos, including variations of illumination, noise, resolution, angle of video cameras and weather. The vehicles are divided into six categories, i.e., bus, car, motorcycle, minibus, truck and van. The performance evaluation is carried out on the VehicleDataset. The experimental results show that the proposed method can avoid the complicated process of manually extracting features and the average classification accuracy is up to 98.26%, which is 3.42% higher than the conventional methods using "Feature + Classifier".

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