Object Tracking in the Video Stream by Means of a Convolutional Neural Network
Yu. N.
Zolotukhin
1
K. Yu.
Kotov
2 *
A. A.
Nesterov
1
E. D.
Semenyuk
1
1Institute of Automation and Electrometry, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630090
Russia
2Institute of Automation and Electrometry, Siberian Branch, Russian Academy of Sciences,Novosibirsk, 630090
Russia
Correspondence to:
*e-mail: kotov@idisys.iae.nsk.su
March 3, 2020
Abstract—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.
Keywords:
trackingvideo streamconvolutional neural networkKalman filter
DOI: 10.3103/S8756699020060163