Pedestrian Tracking with Monocular Camera using Unconstrained 3D Motion Model
This addresses pedestrian tracking for applications like surveillance or autonomous driving by offering a more flexible 3D motion model, though it appears incremental as it builds on existing filtering techniques.
The paper tackled pedestrian tracking with a monocular camera by proposing a first-principle single-object model that does not constrain motion to a ground plane, using an unscented Kalman filter and testing on the MOT-17 dataset, yielding promising 3D results with perfect 2D projections and matching error covariance.
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.