SSNet: Saliency-SelectNet for Vehicle Re-Identification
Author:
Affiliation:
1. Research Center of Intelligent Transportation System, School of Intelligent Systems Engineering, Guangdong Provincial Key Lab of Intelligent Transportation System, Sun Yat-Sen Univ., Guangzhou, Guangdong, China.
Publisher
American Society of Civil Engineers
Link
http://ascelibrary.org/doi/pdf/10.1061/9780784483138.024
Reference18 articles.
1. Group-Sensitive Triplet Embedding for Vehicle Reidentification.;Bai Y. L., F. G;IEEE Trans. Multimedia.,2018
2. Bay T. T. L. G. V. 2006. “SURF: speeded up robust features.” European Conference on Computer Vision Graz Austria: Springer Berlin 404–417.
3. Chen Y. H. L. W. et al. 2010. “The algorithm of vehicles trajectory extraction based on video image sequences.” International Congress on Image and Signal Processing Yantai China: IEEE 1018–1022.
4. Theory of communication. Part 1: the analysis of information.;Gabor;J. Inst. Electr. Eng.,1946
5. Gamage J. G. S. A. A. P. 2014. “GPU based non-overlapping multi-camera vehicle tracking.” International Conference on Information and Automation for Sustainability Colombo Sri Lanka: IEEE 10.1109/ICIAFS.2014.7069550.
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