A real-time multi-class multi-object tracker using YOLOv2
KangUn Jo, JungHyuk Im, Jingu Kim, Dae‐Shik Kim · 2017
Multi-class multi-object tracking is an important problem for real-world applications like surveillance system, gesture recognition, and robot vision system. However, building a multi-class multi-object tracker that works in real-time is difficult due to low processing speed for detection, classification, and data association tasks. By using fast and reliable deep learning based algorithm YOLOv2 together with fast detection to tracker algorithm, we build a real-time multi-class multi-object tracking system with competitive accuracy.