This paper uses multi-object tracking methods known from the radar tracking community to address the problem of pedestrian tracking using 2D bounding box detections. The standard point-object (SPO) model is adopted, and the posterior density is computed using the Poisson multi-Bernoulli mixture (PMBM) filter. The selection of the model parameters rooted in continuous time is discussed, including the birth and survival probabilities. Some parameters are selected from the first principles, while others are identified from the data, which is, in this case, the publicly available MOT-17 dataset. Although the resulting PMBM algorithm yields promising results, a mismatch between the SPO model and the data is revealed. The model-based approach assumes that modifying the problematic components causing the SPO model-data mismatch will lead to better modelbased algorithms in future developments.

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 excellent 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.

Common visual tracking algorithms make use of measurement models whose parameters need to be specified. These are, namely, measurement noise covariance related to spatial error of detections provided by a visual detection algorithm, probability of detection, and expected number of clutter detections. The measurement model parameters are often hand selected, using no data-based knowledge. This paper proposes a technique to estimate the parameters by reliably associating detections to annotations in each video frame. The technique is verified on the publicly available MOT-17 dataset.

Common visual tracking algorithms make use of bounding box (BB) motion models. These models are parameterized by quantities such as noise covariance parameters corresponding to the evolution of the position, velocity, aspect ratio, width, or height of the bounding box, or their respective velocities. The noise covariance parameters are often hand-selected, using no principled knowledge regarding various aspects such as camera pose or frame rate. This paper aims to analyze how these aspects influence parameter estimates obtained from annotated datasets that are well-known to the visual tracking community. To obtain the estimates, the recently developed measurement difference method (MDM) is modified and used.