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.

提出了一个基于第一原理的单物体模型用于行人追踪，在该模型中，可以通过已知的 3D 统计信息（如行人身高）来描述移动物体的范围。该模型不需要将 3D 中的物体运动约束到一个共同的地平面上，这在 3D 视觉跟踪应用中很常见。使用无迹卡尔曼滤波器（UKF）实现了这一模型，并在公开可用的 MOT-17 数据集上进行了测试。该解决方案在 3D 中产生了令人满意的结果，同时在二维图像中保持了完美的结果。此外，估计的误差协方差与真实值相匹配。与传统方法不同，引入的模型参数具有方便的含义并且可以轻松调整以适应问题。