Abstract
Pedestrian retrieval is an important technique of searching for a specific pedestrian from a large gallery. In this paper, we introduce three types of distance metrics for pedestrian retrieval, including learning-free distance metric methods, metric learning methods, and convolution neural network (CNN) methods, and evaluate the performance of different distance metrics using the Market-1501 database. The experiment shows that the CNN methods achieve the best results.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant No. 61711530240 and No. 61501327, Natural Science Foundation of Tianjin under Grant No. 17JCZDJC30600 and No. 15JCQNJC01700, the Fund of Tianjin Normal University under Grant No.135202RC1703, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 201700001 and No. 201800002, the China Scholarship Council No. 201708120039 and No. 201708120040, the NSFC-Royal Society grant, and the Tianjin Higher Education Creative Team Funds Program.
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Zhang, Z., Huang, M., Liu, S., Durrani, T.S. (2020). Evaluation Distance Metrics for Pedestrian Retrieval. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_140
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DOI: https://doi.org/10.1007/978-981-13-6504-1_140
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