Multiple object tracking package
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 open source software.
It 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.
To build, use:
To run, use:
L.M. Murray and T.B. Schön (2018). Automated learning with a probabilistic programming language: Birch. Annual Reviews in Control 46:29--43. [arxiv]
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).