On June 23rd, we discussed “*Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model*” by Edward Vul, Michael C Frank, George Alvarez, and Joshua B Tenenbaum.

This paper proposes a dynamic probabilistic model as an ideal observer for multiple object tracking. The ideal observer model uses a set of *n* Kalman filters to track *n* objects. Observations at each time step are composed of *n* pairs of position and velocity values. However, inference must be performed in order to associate each observation pair with the appropriate object identity (Kalman filter).