使用无约束3D运动模型的单目相机行人跟踪
Pedestrian Tracking with Monocular Camera using Unconstrained 3D Motion Model
AI总结：
该研究提出一种基于第一性原理的无约束3D运动单目标行人跟踪模型，采用UKF实现非线性滤波，经MOT-17测试，3D跟踪效果良好且2D投影性能优异，参数易调整。
摘要：
本文提出了一种基于第一性原理的单目标行人跟踪模型。该模型假设可通过已知的3D统计量（如行人身高）描述运动物体的外形范围，因此无需像常见3D视觉跟踪应用那样，将物体的3D运动约束在公共地平面上。研究采用无迹卡尔曼滤波（UKF）实现了该模型的非线性滤波器，并使用公开数据集MOT-17进行测试。结果显示，所提方案在3D跟踪上取得了良好效果，同时投影到2D图像时仍保持优异性能，且估计误差协方差与真实误差协方差一致。与传统方法不同，该模型的参数物理意义明确，可方便地针对具体问题调整。
英文摘要：
A first-principle single-object model is proposed for pedestrian tracking. It is assumed that the extent of the moving object can be described via known statistics in 3D, such as pedestrian height. The proposed model thus need not constrain the object motion in 3D to a common ground plane, which is usual in 3D visual tracking applications. A nonlinear filter for this model is implemented using the unscented Kalman filter (UKF) and tested using the publicly available MOT-17 dataset. The proposed solution yields promising results in 3D while maintaining perfect results when projected into the 2D image. Moreover, the estimation error covariance matches the true one. Unlike conventional methods, the introduced model parameters have convenient meaning and can readily be adjusted for a problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
