Fine-grained vehicle recognition using hierarchical fine-tuning strategy for Urban Surveillance Videos

The Fine-grained Vehicle recognition is easily affected by small visual changes. The existing recognition methods have less robustness to these conditions (such as illumination, weather changes, etc.) and the accuracy of vehicle recognition in complex environments cannot achieve a satisfying result. In this paper, a high-accuracy fine-grained vehicle recognition method using Convolutional Neural Network for urban surveillance videos is proposed. Differing from the conventional CNN based methods, which usually obtain a single classification model, the proposed method combines Pre-training and hierarchical fine-tuning to obtain different classification model that can adapt to the change of illumination conditions. In the recognition phase, the input image is evaluated firstly using the proposed image quality assessment method. Then, the classification model is selected adaptively according to the results of quality evaluation and used for recognition. To test our method, we use a publicly vehicle recognition dataset for Urban Surveillance Videos provide by Yang. Experimental results show that the accuracy of the proposed method is up to 98.89%, which is 0.5% higher than Yang's method.