Person Re-Identification Using Deep Learning Approach: A Survey
Keywords:Re-Ranking, Deep Learning, Machine Learning, Distance Measurement Similarity, Hash Function
Person Re-identification proof crosswise over various observation cameras with disjoint fields of view has turned out to be one of the most intriguing and testing subjects in the zone of wise video observation. Albeit a few techniques have been created, what's more, proposed, certain confinements and uncertain issues remain. In the majority of the current Re-identification proof approaches, highlight vectors are separated from sectioned still pictures or video outlines. Distinctive similitude or disparity measures have been applied to these vectors. A few strategies have utilized basic consistent measurements, while others have used models to acquire streamlined measurements. Some have made models dependent on neighborhood shading or surface data, and others have constructed models dependent on the stride of individuals. When all is said in done, the primary goal of every one of these methodologies is to accomplish a higher-exactness rate and lower computational costs.
A. Bedagkar-Gala and S. K. Shah, “A survey of approaches and trends in person re-identification,” Image and vision computing, Vol. 32, No. 4, pp. 270-286, 2014.
L. Zheng, Y. Yang and A. G. Hauptmann, “Person re-identification: Past, present, and future,” arXiv preprint arXiv: 1610.02984, 2016.
L. Wu, Y. Wang, X. Li and J. Gao, "What-and-where to match: Deep spatially multiplicative integration networks for person re-identification,” Pattern Recognition, Vol. 76, pp.727-738, 2018.
D. Chen, Z. Yuan, B. Chen and N. Zheng, “Similarity learning with spatial constraints for person re-identification,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1268-1277, 2016.
R. Mazzon, S. F. Tahir and A. Cavallaro, “Person re-identification in crowd,” Pattern Recognition Letters, Vol. 33, No. 14, pp.1828-1837, 2012.
M. Ye, C. Liang, Y. Yu, Z. Wang, Q. Leng, C. Xiao, J.Chen and R. Hu, “Person re-identification via ranking aggregation of similarity pulling and dissimilarity pushing,” IEEE Transactions on Multimedia, Vol. 18, No. 12, pp. 2553-2566, 2016.
Y. Fu, Y. Wei, Y. Zhou, H. Shi, G. Huang, X. Wang, Z. Yao and T. Huang, “Horizontal pyramid matching for person re-identification,” In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, No. 01, pp. 8295-8302, 2019.
Y. Liu, N. Song and Y. Han, “Multi-cue fusion: Discriminative enhancing for person re-identification”, Journal of Visual Communication and Image Representation, Vol. 58, pp. 46-52, 2019.
Y. Lin, L. Zheng, Z. Zheng, Y. Wu, Z. Hu, C. Yan, and Y. Yan, “Improving person re-identification by attribute and identity learning.”Pattern Recognition, Vol. 95, pp. 151-161, 2019.
M. Yuan, D. Yin, J. Ding, Z. Zhou, C. Zhu, R. Zhang, and A. Wang, “A multi-image Joint Re-ranking framework with updateable Image Pool for person re-identification,” Journal of Visual Communication and Image Representation, Vol. 59, pp. 527-536, 2019.
X. Liu, S. Bi, X. Ma and J. Wang, “Multi-Instance Convolutional Neural Network for multi-shot person re-identification,” Neuro computing, Vol. 337, pp. 303-314, 2019.
Y. Chen, J. Yuan, Z. Li, Y. Wu, M. Nouioua and G. Xie, “Person re-identification based on re-ranking with expanded k-reciprocal nearest neighbors,” Journal of Visual Communication and Image Representation, Vol. 58, pp. 486-494, 2019.