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mgb45

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A series of notebooks for model-based DRL or optimal control. Includes direct trajectory optimisation, CEM policy training and CEM planning.

ECE4076

Interactive class notebooks for ECE4076 Computer Vision.

Jupyter Notebook

MoGapFill

Low dimensional Kalman smoother that fills gaps in motion capture marker trajectories

Python

ECE3091_resources

A selection of code resources for mobile robot navigation and control.

Jupyter Notebook

facetracking

Opencv face tracking in ROS (Haar wavelets + KF tracking)

C++

action_permutations

Predicting action sequences with visual permutations

Jupyter Notebook

bayes_opt_navigation

Informative path planning for a mobile robot using optimal control and Bayesian optimisation

Jupyter Notebook

NVAE

MWE of Newtonian VAE

Jupyter Notebook

measurementproposals

Measurement proposals for upper body pose estimation

C++

SignerBodyPose

Upper body joint tracking for the RWTH-Phoenix signer database using a Mixture Kalman filter

MATLAB

mkfbodytracker_pdaf

Fast upper body pose estimation

C++

neural-data-association

Learning data association without data association (using smoothness assumptions in observations)

Jupyter Notebook

SDN_Pendulum

Switching density network pendulum example https://arxiv.org/abs/1907.04360

Jupyter Notebook

gmm_training

Mixture of random walk motion model training for upper body pose estimation

C++

conf-scheduler

Automatic conference session assignment using latent topic model features

Jupyter Notebook

mgb45/OC-notebooks

mgb45

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