Title: Image tracking and matching algorithm of semi-dense optical flow method

Authors: Tao Song; Li-Bo Cao; Ming-Fu Zhao; Yu-Hang Luo; Xin Yang; Shuai Liu

Addresses: Chongqing Key Laboratory of Modern Photoelectric Detection Technology and Instrument, Chongqing University of Technology, Chongqing 400054, China ' Chongqing Key Laboratory of Modern Photoelectric Detection Technology and Instrument, Chongqing University of Technology, Chongqing 400054, China ' Chongqing Key Laboratory of Modern Photoelectric Detection Technology and Instrument, Chongqing University of Technology, Chongqing 400054, China ' Chongqing Key Laboratory of Modern Photoelectric Detection Technology and Instrument, Chongqing University of Technology, Chongqing 400054, China ' Chongqing Key Laboratory of Modern Photoelectric Detection Technology and Instrument, Chongqing University of Technology, Chongqing 400054, China ' Chongqing Key Laboratory of Modern Photoelectric Detection Technology and Instrument, Chongqing University of Technology, Chongqing 400054, China

Abstract: The traditional optical flow method is based on the assumption of spatial consistency of the optical flow field. It is easy to reduce the tracking quality and even lead to the loss of target tracking in the areas of image feature missing, boundary and occlusion. A semi-dense optical flow method is proposed to realise stable tracking of image features. Firstly, the feature points are preserved by calculating the pixel points with large change of pixel gradient in the image; Secondly, according to the principle of grey level invariance, the grey level difference function between the corresponding feature points of adjacent frames is constructed; Finally, the gradient descent principle is used to optimise the grey difference function and realise the accurate matching of feature points of adjacent frames. The results show that compared with the traditional LK optical flow method, this algorithm can effectively improve the feature tracking capability, and at the same time can effectively retain the useful information in the image. Compared with the traditional feature point matching method, the algorithm presented in this paper has an efficient operation rate.

Keywords: optical flow method; half dense; image processing; feature tracking; image matching.

DOI: 10.1504/IJWMC.2021.113228

International Journal of Wireless and Mobile Computing, 2021 Vol.20 No.1, pp.93 - 98

Received: 20 Aug 2020
Accepted: 11 Nov 2020
Published online: 24 Feb 2021 *

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