International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Deep Learning Based Multi-Camera Person Tracking and Re-identification System

Author(s) Mudipalli Varun Kumar, Mohammed Kabeeruddin Ansari, Mohammad Kaif Ali, P. Dastagiri Reddy
Country India
Abstract This research paper presents a novel Deep Learning Based Multi-Camera Person Tracking and Re-identification System designed to enhance security and surveillance in public spaces. Leveraging advanced technologies including Object Detection through YOLO and Deep SORT, the system offers an efficient solution for monitoring and tracing individuals across multiple camera views. Through object detection, individuals are identified within each camera frame, while Deep SORT ensures seamless tracking as they move across different camera perspectives. Additionally, a person re-identification module enhances tracking accuracy by extracting distinctive features and linking individuals across various camera views using unique identifiers. This system represents a significant advancement in surveillance capabilities, contributing to the development of effective and user-friendly surveillance solutions.
Keywords YOLO, Person Tracking, Object Detection, Deep SORT, Re-identification, Deep Learning
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-02-23
Cite This Deep Learning Based Multi-Camera Person Tracking and Re-identification System - Mudipalli Varun Kumar, Mohammed Kabeeruddin Ansari, Mohammad Kaif Ali, P. Dastagiri Reddy - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.13904
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.13904
Short DOI https://doi.org/gtjtt2
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