Pedestrian Tracking with Monocular Camera using Unconstrained 3D Motion
  Model
Jan Krejčí Oliver Kost Ondřej Straka Jindřich Duník
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.