A Robust Real-Time Detecting and Tracking Framework for Multiple Kinds of Unmarked Object.

ABSTRACT: A rodent real-time tracking framework is proposed to automatically detect and track multi-objects in real time and output the coordinates of each object, which combines deep learning (YOLO v3: You Only Look Once, v3), the Kalman Filter, improved Hungarian algorithm, and the nine-point position correction algorithm. A model of a Rat-YOLO is trained in our experiment. The Kalman Filter model is established in an acceleration model to predict the position of the rat in the next frame. The predicted data is used to fill the losing position of rats if the Rat-YOLO doesn't work in the current frame, and to associate the ID between the last frame and current frame. The Hungarian assigned algorithm is used to show the relationship between the objects of the last frame and the objects of the curren

SUBMITTER: Lv X 

PROVIDER: S-EPMC6982905 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature