Title: Three-dimensional dynamic tracking learning algorithm for pedestrians on indefinite shape base based on deep learning

Authors: Yaomin Hu

Addresses: School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou 511483, China

Abstract: In order to improve the three-dimensional dynamic tracking and recognition ability to pedestrians, a three-dimensional dynamic tracking learning algorithm for pedestrians on indefinite shape base based on deep learning is proposed in this paper. First, the indefinite shape base mesh of body imaging is segmented to extract three-dimensional dynamic similarity features of pedestrians, and the three-dimensional feature points are marked; the deep learning method is adopted for fusion of gray pixel value and extraction of difference feature to images during three-dimensional dynamic tracking. Then a motion vector library is constructed based on the extraction results, and the template matching equation of three-dimensional dynamic feature points of pedestrians is obtained. The simulation results show that this method can accurately track moving bodies in three-dimensional dynamic tracking and recognition and can provide good robustness in moving body target extraction with accuracy up to 100% at maximum and detection time of 48.83ms at maximum.

Keywords: indefinite shape base; pedestrian; three-dimensional dynamic tracking; deep learning; image.

DOI: 10.1504/IJICT.2019.102474

International Journal of Information and Communication Technology, 2019 Vol.15 No.2, pp.107 - 120

Received: 14 Feb 2018
Accepted: 21 Mar 2018

Published online: 27 Sep 2019 *

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