xiaofeng-ustc / pmbm

Python implementation of Poisson Multi-Bernoulli Mixture Filter for Multi-Object Tracking.

PMBM

This is the implementation of the Poisson Multi Bernoulli Mixture Filter for the Master Thesis Multi-Object Tracking using either Deep Learning or PMBM filtering by Erik Bohnsack and Adam Lilja at Chalmers University of Technology, spring of 2019.

The implementation is done in Python 3.7 and it has only been tested on Ubuntu 16.04 and MacOS 10.13.6.

Requirements

python 3.7

1. Get Murty-submodule git submodule update
2. Install Murtypip3 install ./murty
3. filterpy pip3 install filterpy
4. motmetrics pip3 install motmetrics
5. deap pip3 install deap

Results in gif-format

KITTI training sequence 20. Simulated object detections with noise, clutter and miss detections. Constant Acceleration motion model.

KITTI training sequence 16. Simulated object detections with noise, clutter and miss detections. Constant Acceleration motion model.

Run

Check runforrest.ipynb