Multi-target detection & tracking using machine learning methodologies

This thesis presents a comprehensive study on multi-target human detection and tracking using machine learning methodologies, with a focus on integrating data from multiple camera sources. As the demand for advanced surveillance systems and automated monitoring increases, effective detection and tracking of individuals in dynamic environments become paramount. This research explores the implementation of state-of-the-art deep learning models, such as YOLO (You Only Look Once) and Faster R-CNN, for real-time human detection across varied environments. Building on existing methodologies, we introduce an innovative framework that not only detects and tracks primary individuals but also identifies and categorizes newly detected unknown individuals as "sub persons of interest." This hierarchical approach allows for enhanced relationship management between individuals and improves the accuracy of identity retention over time, especially in crowded or occluded scenarios. The proposed system employs advanced data association techniques, such as the Hungarian algorithm and Deep SORT, to seamlessly link detections across frames from multiple cameras while managing identity switches effectively. Evaluations conducted on diverse datasets highlight the effectiveness of the proposed method, demonstrating its robustness and scalability in real-world applications. Our findings indicate that leveraging multi-camera inputs significantly enhances detection and tracking performance, providing improved situational awareness. This research contributes to the growing field of computer vision by addressing current limitations in human detection and tracking systems, paving the way for future advancements in surveillance technologies and intelligent monitoring solutions. This work lays foundational concepts for future research into behavioral analysis and interaction recognition, further contextualizing the relationships between individuals in complex environments. Keywords: Multi-target tracking, Machine learning, Surveillance system