Real-Time Multiple Object Tracking and Object Detection using YOLO v7 and FairMOT Algorithm

Abstract

This research presents a comprehensive approach to real-time motion tracking and object detection through the seamless integration of the YOLO v7 architecture with the FairMOT algorithm. The objective of this study is to provide a pragmatic solution that not only advances the state-of-the-art in these techniques but also facilitates their practical deployment across diverse domains, including surveillance, robotics, and autonomous systems. The YOLO v7 model is renowned for its rapid and precise object localization and classification capabilities, making it an ideal foundation for object detection. In conjunction with the FairMOT algorithm, which excels in real-time multiple object tracking, this integration forms a robust framework for delivering a holistic real-time solution. Rigorous evaluations, conducted against benchmark datasets, demonstrate the effectiveness of this approach, highlighting its ability to adeptly track multiple objects, even in intricate scenarios characterized by occlusions and dynamic motion variations. The collaborative synergy between the FairMOT algorithm and YOLO v7 architecture significantly enhances tracking accuracy while maintaining computational efficiency. Our integration endeavors to address real-world challenges and offer a tool that simplifies complex tasks across various applications. This research not only pushes the boundaries of real-time motion tracking and object detection but also extends the horizons of practical utility.