A simple multiple object tracking demonstration in the Birch probabilistic programming language. This demonstrates the use of a universal probabilistic programming language for inference on a model without fixed dimension (the number of objects is unknown). Data is simulated from the model and then filtered using a particle filter, within which the delayed sampling heuristic (Murray et al. 2018) automatically yields a Kalman filter for the tracking of each object. It is used as an example in Murray & Schön (2018), in which further details are available.
This package is licensed under the Apache License, Version 2.0 (the "License"); you may not use it except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.
This package requires the Birch.Cairo package, which should be installed first.
To build and install, use:
birch build
birch install
To run, use:
./run.sh
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L.M. Murray and T.B. Schön (2018). Automated learning with a probabilistic programming language: Birch. Annual Reviews in Control 46:29--43. [arxiv]
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L.M. Murray, D. Lundén, J. Kudlicka, D. Broman and T.B. Schön (2018). Delayed Sampling and Automatic Rao–Blackwellization of Probabilistic Programs. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS).