Open AccessA Real-time Multi-target tracking method based on Deep Learning
Author(s) -
Sitong Sun,
Yu Wang,
Yan Piao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1920/1/012112
Subject(s) - artificial intelligence , computer science , computer vision , tracking (education) , intersection (aeronautics) , kalman filter , frame (networking) , data association , similarity measure , similarity (geometry) , bipartite graph , graph , filter (signal processing) , image (mathematics) , aerospace engineering , psychology , telecommunications , pedagogy , theoretical computer science , engineering
In view of complex model and poor real-time performance of current multi-target tracking algorithms, a real-time online multi-target tracking method based on deep learning is proposed. Firstly, the detector is used to detect the target in video image space and obtain its detection frame. After that, the position, coordinates and motion of the next frame of the target are predicted by the Kalman filter. Then Complete Intersection over Union (CIOU) is used as the distance measure to calculate the overlap between the detection box and the prediction box, and further generate the similarity matrix. Finally, the Hungarian algorithm finds optimal matching in the bipartite graph formed by the two boxes, so as to realize the data association between multiple targets. Experimental evaluation shows that this method finishes real-time and online multi-target tracking well.