Object Tracking in the Video Stream by Means of a Convolutional Neural Network

被引：1
|
作者：
Zolotukhin, Yu N. [1 ]
Kotov, K. Yu [1 ]
Nesterov, A. A. [1 ]
Semenyuk, E. D. [1 ]
机构：
[1] Russian Acad Sci, Inst Automat & Electrometry, Siberian Branch, Novosibirsk 630090, Russia
来源：
OPTOELECTRONICS INSTRUMENTATION AND DATA PROCESSING | 2020年 / 56卷 / 06期
基金：
俄罗斯基础研究基金会;
关键词：
tracking; video stream; convolutional neural network; Kalman filter;
D O I：
10.3103/S8756699020060163
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
A new algorithm of 6-coordinate tracking of a moving object on a sequence of RGB-images that is based on the convolutional neural network is proposed. Training the neural network is carried out by using the synthesized data of the object with a dynamic model of motion. A Kalman filter is included into the feedback from the network output to its input to obtain a smoothed estimate of the object coordinates. Preliminary results of object tracking on synthesized images demonstrates the efficiency of the proposed approach.
引用
收藏
页码：642 / 648
页数：7
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