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Mendes, D., Correia, S., Jorge, P., Brandão, T., Arriaga, P. & Nunes, L. (2023). Multi-camera person re-identification based on trajectory data. Applied Sciences. 13 (20)

D. A. Mendes et al.,  "Multi-camera person re-identification based on trajectory data", in Applied Sciences, vol. 13, no. 20, 2023

@article{mendes2023_1715516274514,
	author = "Mendes, D. and Correia, S. and Jorge, P. and Brandão, T. and Arriaga, P. and Nunes, L.",
	title = "Multi-camera person re-identification based on trajectory data",
	journal = "Applied Sciences",
	year = "2023",
	volume = "13",
	number = "20",
	doi = "10.3390/app132011578",
	url = "https://www.mdpi.com/2076-3417/13/20/11578"
}

TY  - JOUR
TI  - Multi-camera person re-identification based on trajectory data
T2  - Applied Sciences
VL  - 13
IS  - 20
AU  - Mendes, D.
AU  - Correia, S.
AU  - Jorge, P.
AU  - Brandão, T.
AU  - Arriaga, P.
AU  - Nunes, L.
PY  - 2023
SN  - 2076-3417
DO  - 10.3390/app132011578
UR  - https://www.mdpi.com/2076-3417/13/20/11578
AB  - This study presents a trajectory-based person re-identification algorithm, embedded in a tool to detect and track customers present in a large retail store, in a multi-camera environment. The customer trajectory data are obtained from video surveillance images captured by multiple cameras, and customers are detected and tracked along the frames that compose the videos. Due to the characteristics of a multi-camera environment or the occurrence of occlusions, caused by objects such as shelves or counters, different identifiers are assigned to each person when, in fact, they should be identified with a unique identifier. Thus, the proposed tool tries to solve this problem in a scenario where there are constraints in using images of people due to data privacy concerns. The results show that our method was able to correctly re-identify the customers present in the store with a re-identification rate of 82%.
ER  -