电讯技术2018，Vol.58Issue(1)：6-12,7.DOI:10.3969/j.issn.1001-893x.2018.01.002
基于区域卷积神经网络和光流法的目标跟踪
Object Tracking Based on Region Convolution Neural Network and Optical Flow Method
摘要
Abstract
In order to solve the problem of slow speed of online target tracking based on deep learning,a target tracking algorithm based on regional convolutional network and optical flow method is designed and implemented.Based on the T-1 frame tracking results,the optical flow method is used to calculate the tracking target's motion vector to calculate the primary selection box of the tracking target on the T frame,and the primary selection box is used as the input of the regional convolutional network to calculate accurate tracking of the target results.Through experimental analysis and comparison,the algorithm has good robustness to the target velocity and deformation,and the tracking speed can reach 50 frame/s.Compared with the on-line tracking algorithm,the proposed algorithm improves the speed of target tracking algorithm greatly while satisfying the high tracking accuracy.关键词
目标跟踪/深度学习/卷积神经网络/光流法Key words
object tracking/deep learning/convolutional neural network/optical flow分类
信息技术与安全科学引用本文复制引用
吴进,董国豪,李乔深..基于区域卷积神经网络和光流法的目标跟踪[J].电讯技术,2018,58(1):6-12,7.基金项目
国家自然科学基金资助项目(61634004,61602377) （61634004,61602377）
陕西省科技统筹创新工程项目(2016KTZDGY02-04-02) （2016KTZDGY02-04-02）
陕西省重点研发计划(2017GY-060) （2017GY-060）
