Decentralized Text-Based Person Re-Identification in Multi-Camera Networks

Text-based person re-identification (re-ID) is an emerging research domain in multi-camera surveillance systems. It involves identifying individuals across different camera views using textual descriptions as queries. Although text-based person re-ID holds significant potential for surveillance syst...

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Vydané v:IEEE access Ročník 12; s. 172125 - 172148
Hlavní autori: Agyeman, Rockson, Rinner, Bernhard
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy
Algorithms
Application servers
Cameras
Computer architecture
Datasets
Decentralized algorithm
Feature extraction
Human performance
Identification of persons
multi-camera networks
multi-modal re-identification model
Nodes
Parameter identification
Privacy
Prototypes
Real time
real-time video analytics
Scalability
Servers
Surveillance
Surveillance systems
text-based person re-identification
Video
Videos
Visualization
ISSN:2169-3536, 2169-3536
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Shrnutí:Text-based person re-identification (re-ID) is an emerging research domain in multi-camera surveillance systems. It involves identifying individuals across different camera views using textual descriptions as queries. Although text-based person re-ID holds significant potential for surveillance systems, its practical applications are limited by the high computational demands of existing algorithms. This is because most state-of-the-art algorithms prioritize identification accuracy over resource efficiency. Therefore, surveillance systems based on existing algorithms rely heavily on centralized architectures, where a central application server aggregates videos from camera nodes, generates galleries from the videos, and performs person identification. However, such centralized systems face challenges in large-scale deployments, including scalability issues, high bandwidth utilization, and processing bottlenecks. This paper presents a decentralized approach to text-based person re-ID to overcome these limitations. First, it introduces U-TextReIDNet, a resource-efficient model designed to identify persons in multi-person images on the NVIDIA Jetson Nano embedded board. U-TextReIDNet achieves Top-1 accuracies of 54.02% on the CUHK-PEDES dataset and 38.45% on the RSTPReid dataset. With only 38.77 million parameters, U-TextReIDNet is significantly smaller than most existing text-based person re-ID models. Using U-TextReIDNet, we implement a decentralized system that distributes the person identification task across camera nodes, transmitting only videos containing persons of interest to the command station. Additionally, we developed a prototype of this decentralized system, and conducted performance and usability tests using real human subjects. The prototype successfully performs real-time person re-ID, reduces bandwidth utilization, improves system scalability, and eliminates processing bottlenecks.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3501382