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MobileRGBD
MobileRGBD is corpus dedicated to low level RGB-D algorithms benchmarking on mobile platform. We reversed the usual corpus recording paradigm. Our goal is to facilitate ground truth annotation and reproducibility of records among speed, trajectory and environmental variations. As we want to get rid of unpredictable human moves, we used dummies in order to play static users in the environment. Interest of dummies resides in the fact that they do not move between two recordings. It is possible to record the same robot move in order to evaluate performance of detection algorithms varying speed. This benchmark corpus is intended for "low level" RGB-D algorithm family like 3D-SLAM, body/skeleton tracking or face tracking using a mobile robot.
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# RGBD Sync SDK

## RGBD Sync SDK

This RGBD Sync SDK is designed for file access to data (robot information, depth, infrared, video, bodies and faces from a Kinect2, ...) and easy way to read them synchronously
from separate sources almost like in ROS bag.

The RGBD Sync SDK has been used to record the [MobileRGBD corpus](http://MobileRGBD.inrialpes.fr/). It is available for people who want
to work on this corpus, and anyone who need to use it within the term of the LICENSE.


## Content

First this repository contains submodules:
+ [_DataManagement_](https://github.com/Vaufreyd/DataManagement): provide classes to handle all data (timestamped files, video fiels, etc.)
+ [_Drawing_](https://github.com/Vaufreyd/Drawing): classes to draw in OpenCV cv::Mat all data from the corpus. All these classes will resize draws to feat available space.
+ [_Map_](https://github.com/Vaufreyd/Map): dedicated to simple maps build with wall segments.
+ [_Omiscid_](https://github.com/Vaufreyd/Omiscid): Omiscid 3.0b, codename Yggdrasil is a miidleware. We only used here its system abstraction layer and its JSON (de)serialization.

On can also find subfolders:
+ _Kinect_: All classes dedicated to Kinect2. Under Windows, there will be classes to read, to record and/or to process data. Under Linux, everything is define to read Kinect2 data from MobileRGBD recordings.
+ [**_GenerateVideoFromRecords_**](https://github.com/Vaufreyd/RGBDSyncSDK/tree/master/GenerateVideoFromRecords): an example to show how to synchronously read data from the corpus and generate a (composite) mp4 video.
+ [**_RecordKinect_**](https://github.com/Vaufreyd/RGBDSyncSDK/tree/master/RecordKinect): a project to make your own records using the Kinect2 device under Windows (RGB, Depth, Infrared, Body, Face, Audio). This project uses native Kinect2 SDK under Windows. Your records will be readable using RGBDSyncSDK under Windows/Linux/Mac OSX.

## Cloning and updating

As we used submodules in our project, in order to clone this repo, you must ask git to work recursively:

$> git clone --recursive https://github.com/Vaufreyd/RGBDSyncSDK.git

For the same reason, in order to update this repo, you must ask git to work recursively with submodules:

$> git pull --recurse-submodules

## Example

You can start using this SDK using the [_GenerateVideoFromRecords example_](https://github.com/Vaufreyd/RGBDSyncSDK/tree/master/GenerateVideoFromRecords).

## Participate!

You can help us finding bugs, proposing new functionalities and more directly on this website! Click on the "New issue" button in the menu to do that.
You can browse the git repository here on GitHub, submit patches and push requests!

## Licensing

RGBD Sync SDK and submodules are free software; you can redistribute and/or modify them under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.
[Consult the license on the FSF website](http://www.gnu.org/licenses/lgpl-3.0.txt).

If you are a researcher and this software helps you, please cite our publication on MobileRGBD:
+ *MobileRGBD, An Open Benchmark Corpus for mobile RGB-D Related Algorithms*, Dominique Vaufreydaz, Amaury Nègre,
13th International Conference on Control, Automation, Robotics and Vision, Dec 2014, Singapore, 2014. [(go to author version)](https://hal.inria.fr/hal-01095667)

Copyright (c) 2015, University of Grenoble Alpes and Inria, All rights reserved.
Dominique Vaufreydaz

2017 03 21
The size of this dataset is more than 4000 Mb
Url of the dataset
Related publications
Other metadata
  • External Identifiers:

  • Subjects:

    Computer Science
  • Keywords:

    benchmark, RGB-D, robot
  • Corresponding tasks:

    classification, fall detection, person detection
  • Encoding data format:

    raw

@inproceedings{vaufreydaz:hal-01095667, TITLE = {{MobileRGBD, An Open Benchmark Corpus for mobile RGB-D Related Algorithms}}, AUTHOR = {Vaufreydaz, Dominique and N{`e}gre, Amaury}, URL = {https://hal.inria.fr/hal-01095667}, BOOKTITLE = {{13th International Conference on Control, Automation, Robotics and Vision}}, ADDRESS = {Singapour, Singapore}, YEAR = {2014}, MONTH = Dec, PDF = {https://hal.inria.fr/hal-01095667/file/CorpusPaper.pdf}, HAL_ID = {hal-01095667}, HAL_VERSION = {v1}, }, doi:10.18709/PERSCIDO.2017.03.DS55. Published 2017 via Perscido-Grenoble-Alpes;

@inproceedings{vaufreydaz:hal-01095667, TITLE = {{MobileRGBD, An Open Benchmark Corpus for mobile RGB-D Related Algorithms}}, AUTHOR = {Vaufreydaz, Dominique and N{`e}gre, Amaury}, URL = {https://hal.inria.fr/hal-01095667}, BOOKTITLE = {{13th International Conference on Control, Automation, Robotics and Vision}}, ADDRESS = {Singapour, Singapore}, YEAR = {2014}, MONTH = Dec, PDF = {https://hal.inria.fr/hal-01095667/file/CorpusPaper.pdf}, HAL_ID = {hal-01095667}, HAL_VERSION = {v1}, }, doi:10.18709/PERSCIDO.2017.03.DS55. Published 2017 via Perscido-Grenoble-Alpes