|Assignment using auction global nearest neighbor|
|Jonker-Volgenant global nearest neighbor assignment algorithm|
|Assignment using k-best global nearest neighbor|
|K-best S-D solution that minimizes total cost of assignment|
|Munkres global nearest neighbor assignment algorithm|
|S-D assignment using Lagrangian relaxation|
|Track-oriented multi-hypotheses tracking assignment|
|Multi-hypothesis, multi-sensor, multi-object tracker|
|Multi-sensor, multi-object tracker using GNN assignment|
|Create object detection report|
|Returns updated track positions and position covariance matrix|
|Obtain updated track velocities and velocity covariance matrix|
|Cluster track-oriented multi-hypothesis history|
|Formulate global hypotheses from clusters|
|Prune track branches with low likelihood|
|Confirm and delete tracks based on recent track history|
|Confirm and delete tracks based on track score|
|Track-oriented MHT branching and branch history|
Estimate and predict object motion using a Linear Kalman filter.
Estimate and predict object motion using an extended Kalman filter.
This example shows how to configure and use the global nearest neighbor (GNN) tracker.
This example shows how to define and use confirmation and deletion logic that are based on history or score.