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基于YOLOX-GMM和SORT-Pest的热红外区域多目标3D轨迹跟踪方法研究

Research on the multitarget 3D trajectory tracking method of in the thermal infrared region based on YOLOX-GMM and SORT-Pest.

作者信息

Qiu Xinrui, Xia Juan, Zeng Ye, Huang Guangwen, Xin Bolai, Jiang Runpeng, Wu Kaixuan, Ma Zhe, Li Jun

机构信息

College of Engineering, South China Agricultural University, Guangzhou, China.

Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China.

出版信息

Front Plant Sci. 2024 Oct 7;15:1403421. doi: 10.3389/fpls.2024.1403421. eCollection 2024.

DOI:10.3389/fpls.2024.1403421
PMID:39435020
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11491355/
Abstract

INTRODUCTION

Studying the behavioral responses and movement trajectories of insects under different stimuli is crucial for developing more effective biological control measures. Therefore, accurately obtaining the movement trajectories and behavioral parameters of insects in three-dimensional space is essential.

METHODS

This study used the litchi pest as the research object. A special binocular vision observation system was designed for nighttime movement. A thermal infrared camera was used for video recording of in a lightless environment. Moreover, a multi-object tracking method based on the YOLOX-GMM and SORT-Pest algorithms was proposed for tracking in thermal infrared images. By obtaining the central coordinates of the two in the video, target matching and 3D trajectory reconstruction in the parallel binocular system were achieved.

RESULTS

Error analysis of the detection and tracking model, as well as the 3D reconstruction model, showed that the average accuracy of detection reached 89.6%, tracking accuracy was 96.9%, and the average error of the reconstructed 3D spatial coordinates was 15 mm.

DISCUSSION

These results indicate that the method can accurately obtain the 3D trajectory and motion parameters of . Such data can greatly contribute to researchers' comprehensive understanding of insect behavioral patterns and habits, providing important support for more targeted control strategies.

摘要

引言

研究昆虫在不同刺激下的行为反应和运动轨迹对于制定更有效的生物防治措施至关重要。因此，准确获取昆虫在三维空间中的运动轨迹和行为参数至关重要。

方法

本研究以荔枝害虫为研究对象。设计了一种用于夜间运动的特殊双目视觉观察系统。使用热红外摄像机在无光环境下进行视频记录。此外，提出了一种基于YOLOX-GMM和SORT-Pest算法的多目标跟踪方法，用于在热红外图像中跟踪。通过获取视频中两只的中心坐标，实现了平行双目系统中的目标匹配和三维轨迹重建。

结果

对检测与跟踪模型以及三维重建模型的误差分析表明，检测的平均准确率达到89.6%，跟踪准确率为96.9%，重建的三维空间坐标平均误差为15毫米。

讨论

这些结果表明，该方法能够准确获取的三维轨迹和运动参数。此类数据可极大地有助于研究人员全面了解昆虫的行为模式和习性，为更具针对性的防治策略提供重要支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/ad50149421d8/fpls-15-1403421-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/e4b7688b9e34/fpls-15-1403421-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/35b5986bdb8e/fpls-15-1403421-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/babd5cd4c3f1/fpls-15-1403421-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/ad50149421d8/fpls-15-1403421-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/a6c1876609a1/fpls-15-1403421-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/f53c0a11a1b6/fpls-15-1403421-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/d563426da53f/fpls-15-1403421-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/0118c5e894e9/fpls-15-1403421-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/f24e16b2fef1/fpls-15-1403421-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/2b5486047d7d/fpls-15-1403421-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/e4b7688b9e34/fpls-15-1403421-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/35b5986bdb8e/fpls-15-1403421-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/babd5cd4c3f1/fpls-15-1403421-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/db688112d504/fpls-15-1403421-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/85d5dd8e9ec8/fpls-15-1403421-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b9/11491355/ad50149421d8/fpls-15-1403421-g013.jpg

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本文引用的文献

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J Insect Sci. 2021 Nov 1;21(6). doi: 10.1093/jisesa/ieab079.
2
Honey bees increase their foraging performance and frequency of pollen trips through experience.蜜蜂通过经验提高其觅食表现和花粉采集频率。
Sci Rep. 2019 May 1;9(1):6778. doi: 10.1038/s41598-019-42677-x.
3
Target tracking and 3D trajectory acquisition of cabbage butterfly (P. rapae) based on the KCF-BS algorithm.基于 KCF-BS 算法的菜粉蝶（Pieris rapae）目标跟踪与三维轨迹获取。
Sci Rep. 2018 Jun 25;8(1):9622. doi: 10.1038/s41598-018-27520-z.