Adaptive Fractional-order Variational Dynamic Optical Flow Selection Algorithm for Target Tracking in Dynamic Scenarios

Xunyang  Liang; Qi  Yang; Yilu  Wang; Shida  Wang; Xingzhuo  Huang

doi:10.6180/jase.202411_27(11).0006

Adaptive Fractional-order Variational Dynamic Optical Flow Selection Algorithm for Target Tracking in Dynamic Scenarios

Mechanical Engineering Computer Science and Information Engineering

$Images of quadratic function at different fractional orders$

Xunyang Liang, Qi YangThis email address is being protected from spambots. You need JavaScript enabled to view it., Yilu Wang, Shida Wang, and Xingzhuo Huang

School of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, China

Received: November 2, 2023
Accepted: November 24, 2023
Publication Date: January 27, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202411_27(11).0006

In this paper, an adaptive fractional-order optical flow selection algorithm with improved ant colony clustering is proposed to address the issues of texture processing and dynamic noise perturbation in simultaneous localization and mapping algorithms in dynamic scenes with strong static assumption theory. The algorithm combines the characteristics of fractional differentiation and sparse optical flow algorithm, and makes full use of the weak texture gradient of the image. The ant colony algorithm is improved by using the elite sharing mechanism, and the improved ant colony algorithm is combined with the clustering algorithm. The experimental results show that the algorithm not only realizes the adaptive selection of the best order, but also achieves better dynamic disturbance differentiation ability through the clustering of feature selection. While distinguishing dynamic and static information effectively, more details of optical flow with weak gradient feature are preserved. The proposed algorithm holds promise for simultaneous localization and mapping systems.

Keywords: dynamic feature; sparse optical-flow; fractional differential; adaptive algorithm; elite sharing mechanism; cohesive clustering

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