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 This email address is being protected from spambots. You need JavaScript enabled to view it. and This email address is being protected from spambots. You need JavaScript enabled to view 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}
}