  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.
