Vehicle Shape and Color Classification Using Convolutional Neural Network
This work addresses vehicle surveillance and video analysis, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackled vehicle re-identification by classifying make/model and color using convolutional neural networks, achieving good classification accuracy on controlled and video datasets.
This paper presents a module of vehicle reidentification based on make/model and color classification. It could be used by the Automated Vehicular Surveillance (AVS) or by the fast analysis of video data. Many of problems, that are related to this topic, had to be addressed. In order to facilitate and accelerate the progress in this subject, we will present our way to collect and to label a large scale data set. We used deeper neural networks in our training. They showed a good classification accuracy. We show the results of make/model and color classification on controlled and video data set. We demonstrate with the help of a developed application the re-identification of vehicles on video images based on make/model and color classification. This work was partially funded under the grant.