Multi-Camera Person Re-Identification Based on Trajectory Data
Source abstract
Abstract
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%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Video Surveillance and Tracking Methods · Automated Road and Building Extraction · Gait Recognition and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
ISCTE—University Institute of Lisbon, 1649-026 Lisbon, Portugal
ISTAR-IUL—Information Sciences, Technologies and Architecture Research Center, 1649-026 Lisbon, Portugal
CIS-ISCTE—Center for Psychological Research and Social Intervention, 1649-026 Lisbon, Portugal
Funding
Fundação para a Ciência e a Tecnologia, I.P. (FCT)
References (18)
Show source-provided references
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