International Conference on Transportation and Development 2020
SSNet: Saliency-SelectNet for Vehicle Re-Identification
Publication: International Conference on Transportation and Development 2020
ABSTRACT
Vehicle re-identification of cross-region surveillance video is one of the current research hotspots, helping to analyze the trajectory and the travel rules of vehicles. In this paper, a feature select vehicle re-identification method without using the license plate is proposed. The method is based on a convolutional neural network (CNN) and is divided into three parts. In the first part, salient features are used in series with the original image as input for training in CNN, greatly enhancing the propagation of robust features. In the second part, the feature map selection mechanism selects and sends the features with low similarity to the next convolutional layer, reducing the propagation of irrelevant features in the network. Finally, the distance metrics between image features are compared and sorted in ascending order, as which of the same ID should be small. Experiments on datasets VRID-1 show that the proposed SSNet significantly outperforms the state-of-the-art methods for vehicle re-identification.
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Information & Authors
Information
Published In
International Conference on Transportation and Development 2020
Pages: 272 - 283
Editor: Guohui Zhang, Ph.D., University of Hawaii
ISBN (Online): 978-0-7844-8313-8
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Aug 31, 2020
Published in print: Aug 31, 2020
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