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aae

admit

Adaptive Mixture of Student-t distributions

ai-toolbox

A C++ framework for MDPs and POMDPs with Python bindings

anomaly-detection-in-surveillance-videos

Real-World Anomaly Detection in Surveillance Videos

anomalydetectioncvpr2018

awesome-radar-perception

A curated list of radar datasets, detection, tracking and fusion

awesome-rl

Reinforcement learning resources curated

bin-to-pcd-python

Converting bin files scanned by LIDAR of autonomous vehicles into pcd files for further processing

bistatic-sea-reflectivity

Matlab NRL model of bistatic sea surface radar reflectivity

blam

blensor

Blender Sensor Simulation

cplex-example

Solving a TSP with the CPLEX C++ API.

cubature

multi-dimensional adaptive integration (cubature) in C

cuda-raytracer

dcscn-super-resolution

A tensorflow implementation of "Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network", a deep learning based Single-Image Super-Resolution (SISR) model.

deep-learning-for-tracking-and-detection

Collection of papers and other resources for object tracking and detection using deep learning

dmp-lwr

This is a Dynamic Movement Primitives application coded in Matlab. For regression Locally Weighted Regression is used. In human movement recognition, I got very good results.

documentation

GHER documentation

dogma

Dynamic Occupancy Grid Maps

dynamic-movement-primitives-and-imitation-learning-robotics

Dynamic movement primitives (DMPs) are a method of trajectory control/planning from Stefan Schaal’s lab. Complex movements have long been thought to be composed of sets of primitive action ‘building blocks’ executed in sequence and \ or in parallel, and DMPs are a proposed mathematical formalization of these primitives. The difference between DMPs and previously proposed building blocks is that each DMP is a nonlinear dynamical system. The basic idea is that you take a dynamical system with well specified, stable behavior and add another term that makes it follow some interesting trajectory as it goes about its business. The DMP differential equations (Transformation System, Canonical System, Non-linear Function) realize a general way of generating point-to-point movements. Imitation learning using linear regression is performed to compute the weight factor W from a demonstrated trajectory dataset, given by a teacher. The quality of the imitation is evaluated by comparing the training data with the data generated by the DMP.

ebl-ar-gm

Empirical-Bayesian-Learning-in-AR-Graphical-Models

extendedobjecttracking

Sensor Data Fusion Group

fmin_adam

Matlab implementation of the Adam stochastic gradient descent optimisation algorithm

gibbs_student_t

A robust gaussian - student t mixture model using gibbs sampling for pulsar timing analysis

git_example

first tst

handong1587.github.io

helios

HELIOS - the Heidelberg LiDAR Operations Simulator - is a software package for interactive real-time simulation and visualisation of terrestrial, mobile and airborne laser scan surveys written in Java.

imm-pda

Python implementation of the Interacting Multiple Models Probabalistic data association filter (IMM-PDA). Tracking targets from noisy RADAR data. This filter deals with multiple motion models in the Extended Kalman filter (EKF). Tuned and tested on simulated and real datasets.

maritime-vrp

Branch-and-price solver for a maritime VRP problem

matbiips

Matlab toolbox for Bayesian inference with interacting particle systems