Tracking

The expected position of all currently tracked objects is predicted. The prediction step uses the object’s position, orientation and velocity to create a hypothesis about the next position of the track. The constant velocity motion model combined with the configured acceleration noise results in predictions which are scattered along the predicted object’s path.

During the prediction step, the existence probability of each track is multiplied by the configured survival probability. This effectively reduces the confidence that the object is still present in the scene.

This step receives the detected objects and matches them to the predicted state of all currently tracked objects. When real measurements align well with the tracking parameters and the configured motion model, the state of the matching track is updated. The updated state will then contain the newly estimated velocity, the measured position, and a derived orientation based on the difference between the last positions.

Each track that could be updated with a new measurement has its existence probability increased.

Each detected object that could not be matched with an existing track is assigned a new track. To ensure consistent identification, the track receives a unique track-id. The new track’s state is initialized using the detected object position and a default value for its existence probability.

Every track, which could not be matched with a detected object, gradually loses existence probability after several tracking cycles. The track is deleted when the existence probability falls below the minimum existence probability threshold.