Effect of depth information on multiple-object tracking in three dimensions: A probabilistic perspective
Fig 3
Simplified model illustration.
(A) NE-cd model run on object trajectories for two objects, including confusion of objects. The red and blue dashed lines represent the actual trajectory for two different objects, the green line indicates the model’s position estimate of one object. The vertical black line indicates the time point of the data used in B. (B) Likelihood of a measurement coming from object 1 (blue) and object 2 (red) in the 2D case (left) and 3D case (right). Contour plots represent four slices of the two-dimensional likelihood function, evenly spread from the minimum to maximum likelihoods. Similar likelihoods in one dimension can be disambiguated in the other dimension. (C) Contour plots of predicted state and covariance given the posterior distribution of previous time step (black) for FE (magenta) and NE-cd (cyan) model, NE-cd does not use velocity information in the prediction, leading to biases towards zero velocity and position. Data was generated with σx = 2° and σv = 0.2° per frame using the best fit parameters from the NE-cd model (see Table 2).