The IASLAB-RGBD Fallen Person Dataset consists of several RGB-D frame sequences containing 15 different people. It has been acquired in two different laboratory environments, the Lab A and Lab B. It can be divided into two parts: the former acquired from 3 static Kinect One V2 placed on 3 different pedestals ; the latter from a Kinect One V2 mounted on our healthcare robot prototype, see "An Open Source Robotic Platform for Ambient Assisted Living" by M. Carraro, M. Antonello et al in AIRO at AI*IA 2015.
Both parts are briefly described in the following. They contain the training/test splits of our approach to detect fallen people.
STATIC DATASET:
- Folder "raw": 360 RGB frames and point clouds with the camera calibrations;
- Folder "segmented_fallen_people": point clouds of the fallen people. They have been manually segmented;
- Folder "training_with_cad_room_and_nyudv2": random selected positives (70%), 24 point clouds from the Lab A and 31 point clouds from the NYU Depth Dataset V2 by Silberman et al;
- Folder "test_with_lab_room": random selected positives (30%) and 32 point clouds from the Lab B.
DYNAMIC DATASET:
- Folder "training": 4 ROS bags with 15932 RGB-D frames in total acquired during 4 robot patrollings of the Lab A;
- Folder "test": 4 ROS bags with 9391 RGB-D frames in total acquired during 4 robot patrollings of the Lab B. This room is more similar to an apartament: spaces are smaller, it is cluttered and contains a sofa;
- Folder "maps": 2D maps of the two environments and ground truth positions of the person centroids in the maps.
Download links:
In the figures below, some RGB samples from both environments are reported:
For questions and remarks directly related to the IASLAB-RGBD Fallen Person Dataset, please contact morris.antonello@dei.unipd.it and marco.carraro@dei.unipd.it.
Licence
This dataset is freely available for academic use.
References
If you use this dataset, please cite the following work:
@inproceedings{antonello2017fast,
title={Fast and Robust detection of fallen people from a mobile robot},
author={Antonello, Morris and Carraro, Marco and Pierobon, Marco and Menegatti, Emanuele},
booktitle={Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on},
year={2017},
organization={IEEE}
}