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Effect of depth information on multiple-object tracking in three dimensions: A probabilistic perspective
Fig 2
Schematic representation of ideal observer MOT model.
White circles indicate the model’s estimate of an object state; black circles indicate their noisy perceptual measurements (indicated by σm). Grey squares indicate the predictions of their future state. 
represents the process noise variance used for prediction. The initial model estimates are set to the objects’ start position. The model proceeds to track the objects for the duration of the trial. This is accomplished by predicting the future state using the process dynamics and combing this with noisy perceptual measurements to determine the measurement assignments. The assignments are used to estimate the objects’ state. The estimates are used to generate predictions and the process is repeated until the end of a trial. At the end of a trial the model is probed to test if objects were correctly tracked. This was done by drawing a random sample from the position of one object and calculating the probability this sample came from a target or non-target.
doi: https://doi.org/10.1371/journal.pcbi.1005554.g002