华南理工大学学报(自然科学版) ›› 2025, Vol. 53 ›› Issue (7): 1-.doi: 10.12141/j.issn.1000-565X.240427

• 机械工程 •    

基于多目近红外视觉的多目标实时跟踪方法

陈忠  王傲辰  高心怡  何利辉  张宪民   

  1. 华南理工大学 机械与汽车工程学院,广东 广州 510640

  • 出版日期:2025-07-25 发布日期:2025-02-21

Multi-Object Real-Time Tracking Method Based on Multi-Modal Near-Infrared Vision

CHEN Zhong  WANG Aochen  GAO Xinyi  HE Lihui  ZHANG Xianmin   

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China

  • Online:2025-07-25 Published:2025-02-21

摘要:

近红外光学跟踪系统能够根据附着于被跟踪物体上的反光标记球实时还原被跟踪物体的运动,目前已被广泛应用于多种领域中。本文提出了一种对目标丢失具有一定鲁棒性的多目近红外目标实时跟踪方法。首先,针对反光标记球在近红外相机中的成像特性,利用灰度质心法提取各个反光标记球的几何中心,然后,在各单目相机中使用SORT算法作为多目标跟踪方法对各个标记点进行帧间匹配;根据对极几何原理结合带权二分图匹配方法确定反光标记球在各个相机中像点的匹配关系,根据三角测量方法实时计算各个受跟踪反光标记球的三维空间坐标。其次根据运动过程中各反光标记球之间的空间位置关系对反光标记球进行分组,识别属于同一物体的反光标记球,并根据同组反光标记球间的欧氏距离建立被跟踪物体与反光标记球的外观特征向量作为物体丢失重现的匹配依据;并完全丢失后再重现的被跟踪物体利用外观特征向量的余弦距离进行重匹配。最后,对所提算法进行实验验证,实验表明,本文算法在不小于60帧的速度下的跟踪精度可达0.5mm;另外可以对丢失的重现物体以及反光标记球进行正确的重匹配。

关键词: 多目视觉, 光学跟踪, 立体匹配

Abstract:

Near-infrared optical tracking systems can restore the movement of tracked objects in real time based on the reflective marker balls attached to the tracked objects. They have been widely used in many fields. This paper proposes a real-time tracking method for multiple near-infrared targets that is robust to target loss. First, according to the imaging characteristics of reflective marker balls in near-infrared cameras, the geometric center of each reflective marker ball is extracted using the grayscale centroid method. Then, the SORT algorithm is used as a multi-target tracking method in each monocular camera to match each marker point between frames. The matching relationship of the image points of the reflective marker balls in each camera is determined based on the principle of epipolar geometry combined with the weighted bipartite graph matching method, and the three-dimensional spatial coordinates of each tracked reflective marker ball are calculated in real time based on the triangulation method. Secondly, the reflective marker balls are grouped according to the spatial position relationship between the reflective marker balls during the movement process, and the reflective marker balls belonging to the same object are identified. The appearance feature vector of the tracked object and the reflective marker ball is established based on the Euclidean distance between the reflective marker balls in the same group as the matching basis for the object loss and reappearance. The tracked object that is completely lost and then reappears is rematched using the cosine distance of the appearance feature vector. Finally, the proposed algorithm is experimentally verified. The experiment shows that the tracking accuracy of the proposed algorithm can reach 0.5mm at a speed of not less than 60 frames. In addition, the lost reproduced objects and reflective marker balls can be correctly re-matched.

Key words: multi-view vision, optical tracking, stereo matching