# YOLORe-IDNet: An Efficient Multi-Camera System for Person-Tracking

arXiv: https://arxiv.org/abs/2309.13387 · HTML: https://mlsanity.com/paper/2309.13387/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 23 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [An Experimental Survey on Correlation Filter-based Tracking](https://mlsanity.com/paper/1509.05520/) (2015)

## Built on by (most cited first, 1 of 1)

- [AaP-ReID: Improved Attention-Aware Person Re-identification](https://mlsanity.com/paper/2309.15780/) (2023)

## Abstract (arXiv)

The growing need for video surveillance in public spaces has created a demand for systems that can track individuals across multiple cameras feeds in real-time. While existing tracking systems have achieved impressive performance using deep learning models, they often rely on pre-existing images of suspects or historical data. However, this is not always feasible in cases where suspicious individuals are identified in real-time and without prior knowledge. We propose a person-tracking system that combines correlation filters and Intersection Over Union (IOU) constraints for robust tracking, along with a deep learning model for cross-camera person re-identification (Re-ID) on top of YOLOv5. The proposed system quickly identifies and tracks suspect in real-time across multiple cameras and recovers well after full or partial occlusion, making it suitable for security and surveillance applications. It is computationally efficient and achieves a high F1-Score of 79% and an IOU of 59% comparable to existing state-of-the-art algorithms, as demonstrated in our evaluation on a publicly available OTB-100 dataset. The proposed system offers a robust and efficient solution for the real-time tracking of individuals across multiple camera feeds. Its ability to track targets without prior knowledge or historical data is a significant improvement over existing systems, making it well-suited for public safety and surveillance applications.