I've developed a project using YOLOv8 for object detection and Deep SORT for object tracking. YOLOv8 is trained on a dataset containing 80 different objects, enabling it to accurately identify a wide range of objects in images or video streams. Its efficient architecture ensures rapid detection of objects across various environments and conditions.
Once objects are detected, Deep SORT takes over to track their movements over time. Deep SORT utilizes advanced techniques to maintain consistent and accurate tracking, even in challenging scenarios such as occlusions, scale variations, or cluttered backgrounds. By associating detections across frames and predicting object trajectories, Deep SORT provides valuable insights into object behaviors and interactions.
By integrating the capabilities of YOLOv8 and Deep SORT, the project offers a comprehensive solution for real-time object detection and tracking tasks. Whether deployed in surveillance systems, autonomous vehicles, or other applications requiring robust object monitoring, this project demonstrates the effectiveness of combining state-of-the-art detection and tracking algorithms.
The synergy between YOLOv8's precise detection and Deep SORT's reliable tracking capabilities ensures optimal performance in diverse environments and scenarios. This project serves as a testament to the power of advanced computer vision techniques in addressing complex tasks efficiently and effectively.