With this system we present a new , non-invasive approach for fallen people detection. Our approach uses only stereo camera data for passively sensing the environment. The key novelty is a human fall detector which uses a CNN based human pose estimator in combination with stereo data to reconstruct the human pose in 3D and estimate the ground plane in 3D. We have tested our approach in different scenarios covering most activities elderly people might encounter living at home. Based on our extensive evaluations, our systems shows high accuracy and almost no miss-classification.
Click here for the associated paper.
- ROS Kinetic
- openpose-ros and all its requirements
cdto your catkin workspacecd src/github clone https://github.com/solbach/fallen-person-detectorcd ../catkin_makesource devel/setup.bash- needs to be done with every new terminal running the fallen person detector
roscorerosrun openpose-ros openpose-ros-noderosrun fallen_person_detector fallen_person_detector_node
- Ubuntu 16.04
- ROS Kinetic
- CUDA 8.0
- cuDNN 6.0
- OpenCV 3.2
Please cite this paper if you used it in your research:
Solbach, Markus D., and John K. Tsotsos.
"Vision-Based Fallen Person Detection for the Elderly."
arXiv preprint arXiv:1707.07608 (2017).
Accepted at ACVR 2017: Updated citation will follow soon.
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