Vehicle Type Recognition in Surveillance Images From Labeled Web-Nature Data Using Deep Transfer Learning

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Vehicle type recognition from surveillance images represents a challenging task in the domain of intelligent monitoring systems. Recently, deep learning methods have been applied to solve this problem. The existing deep learning methods, such as convolutional neural networks (CNN), assume that the training and test data are generated from the same or similar imaging systems. They also require a lot of manual annotations for each task. In this paper, we aim to create an improved deep learning method for vehicle type recognition from surveillance images and propose a system based on CNN and transfer learning. Labeled image data of different types of vehicles are easy to acquire from both vehicle manufacturers and Internet sources. Therefore, our proposed surveillance-based vehicle type recognition system is implemented using only labels from Web data. This allows us to overcome the task of manually labeling the data from surveillance images during the training phase. We need to overcome the gap in the types of vehicles between two different imaging systems. For this, a regularization technique in transfer learning is introduced to the objective function of the traditional convolutional neural network. The proposed method was verified through experiments with the public data set comprehensive cars. The experimental results demonstrate that our proposed recognition method outperforms existing deep learning methods when the training and test data are taken from different imaging systems.