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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal For Multidisciplinary Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
