OBJECT TRACKING IN THE VIDEO STREAM USING A CONVOLUTIONAL NEURAL NETWORK
Yu. N. Zolotukhin, K. Yu. Kotov, A. A. Nesterov, E. D. Semenyuk
Institute of Automation and Electrometry, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia
Keywords: object tracking, video stream, convolutional neural network, Kalman filter
Abstract
In this paper, an algorithm for 6-coordinate moving object tracking on a sequence of RGB images based on a convolutional neural network is proposed. Network training is performed on the synthesized data of an object with a dynamic motion model. A Kalman filter is included into the feedback from the output to the input of the network to obtain a smoothed estimate of the object coordinates. Preliminary results of object tracking on synthesized images demonstrate the effectiveness of the proposed approach.
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