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实时强大的2.5D立体声多对象跟踪与轻量级立体声匹配算法

Jinhyeong Lee¹, Junyoung Shin¹, Eunwoo Park^1,2

¹Department of Mechanical Engineering, Dankook University, Yongin 16890, Republic of Korea.

Sensors (Basel, Switzerland)

|November 13, 2025

概括

本研究介绍了StereoSORT,这是一种实时立体跟踪系统,可以通过轻量级的几何约束来克服阻塞挑战. 它实现了高精度和速度,为资源有限的环境提供了实用解决方案.

科学领域:

计算机视觉计算机视觉
机器人技术机器人技术机器人技术
人工智能的人工智能

背景情况:

单眼视觉系统由于闭塞和断层而难以跟踪物体.
现有的立体视觉追踪方法是计算密集的,需要密集匹配或3D重建.
需要高效准确的立体声多对象跟踪解决方案.

研究的目的:

开发一个实时2.5D立体多对象跟踪框架.
通过使用几何约束来提高跟踪强度,防止堵塞.
为现有的立体声追踪方法提供一个计算效率高的替代方案.

主要方法:

实现了一个轻量级的立体声匹配模块,使用基于直线变换的三角形与界限框坐标.
使用双跟踪器架构进行独立的视图跟踪和重新识别.
采用几何约束来实现强大的通信,而没有外观特征.

主要成果:

立体SORT实现了多重对象跟踪精度 (MOTA) 的0.932和识别F1得分 (IDF1) 的0.823.
性能优于像OC-SORT (IDF1: 0.765) 和ByteTrack (IDF1: 0.609) 这样的单眼追踪器.
实现了 50.1 mm 的中位深度误差和 70 FPS 的处理速度.

结论:

仅凭几何约束就足以实现强大的立体声跟踪,消除了对昂贵外观特征的需求.
在资源有限的环境中,StereoSORT为多对象跟踪提供了实用和高效的解决方案.
该框架显示了与商业传感器相比较的高精度和可靠性.

关键词:

深度估计估计的估计.多对象跟踪多对象跟踪封闭和截断处理处理立体声匹配配对应立体声跟踪跟踪系统立体视觉视觉的立体视觉追踪器重新识别追踪器重新识别

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