Vehicle Classification with Deep Learning

Nowadays, there are many traffic surveillance systems which are installed in almost every city to record events and traffic. The surveillance system is used for various objectives, e.g. vehicles searching and real-time traffic monitoring, etc. For the searching purpose, the system can be used by policeman such as outlaw's vehicle identification in crime. Typically, the officers manually identify the vehicle in recorded video according to its appearances. Although the accuracy of this approach is good, it is time-consuming and inclined to faults due to human fatigue for long duration videos. Moreover, hiring employees is costly. Recently, there are several machine learning methods which can be applied to classify vehicles, e.g. Fuzzy Logic, Decision Tree, Adaboost, Random Forest, Neural Network, etc. Convolutional Neural Network (CNN) is also one of such methods. CNN is a type of Deep Learning which is in the category of the neural network. The method is very well-known in image recognition field at the present because of its performance. In the proposed vehicle classification, there are two vehicle characteristics, i.e. types and colors. Types consist of four classes while colors consist of seven classes. CNN is then used as to classify vehicle images. The experimental results show that CNN can achieve high performance in real-world applications.