Multiple Object Tracking Based on YOLOv5 and Optimized DeepSORT Algorithm
Tianyou Bai · Journal of Physics Conference Series · 2023
Abstract As a widely discussed issue in academic and industrial fields, multiple object tracking (MOT) has a huge impact on various aspects, such as video surveillance, human-machine interaction, viral reality and autonomous driving. Among all the MOT algorithms, DeepSORT enjoys a high reputation for its speed, accuracy and robustness to frequent occlusion circumstance. With the help of DeepSORT, the main objective of this research work is to propose an algorithm with a better performance on frequent occlusion and long-time occlusion issues. To accomplish such a mission, an improved deep appearance descriptor was constructed and trained off-line. To achieve a higher performance, the object detector YOLOv5, which has been proved to be faster than previous detectors, is employed in this research as a substitution of the original detector Faster R-CNN.