Feature selection algorithm for ant colony clustering optimization for target tracking

Yilu Wang; Qi Yang; Xunyang Liang; Shida Wang

doi:10.1117/12.3025908

28 February 2024 Feature selection algorithm for ant colony clustering optimization for target tracking

Yilu Wang, Qi Yang, Xunyang Liang, Shida Wang

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130712D (2024) https://doi.org/10.1117/12.3025908
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

Aiming at the texture processing and noise disturbance problems of simultaneous localization and mapping algorithm for dynamic scenes under the strong static assumption theory, Improved dynamic object tracking based on ant colony clustering (IACDOT) was proposed. The algorithm combines fractional differential and sparse optical flow algorithm to make full use of the weak texture gradient of the image. A dynamic feature search and selection strategy is designed to obtain ant colony clustering, which reduces motion interference and mismatching of dynamic and static features. The experimental results show that the algorithm not only realizes the adaptive selection of pixel gradient order, but also has a better ability to distinguish dynamic disturbance through the clustering of feature selection. It can effectively distinguish motion and static information while retaining more details of weak gradient feature optical flow. The algorithm has a good application prospect in simultaneous localization and mapping system.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yilu Wang, Qi Yang, Xunyang Liang, and Shida Wang "Feature selection algorithm for ant colony clustering optimization for target tracking", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130712D (28 February 2024); https://doi.org/10.1117/12.3025908

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KEYWORDS

Optical flow

Detection and tracking algorithms

Mathematical optimization

Feature selection

Feature extraction

Optical tracking

Image enhancement

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