This repository contains the software necessary to reprocuce the method described in "J. Dybedal and G. Hovland, CNN-based People Detection in Voxel Space using Intensity Measurements and Point Cluster Flattening. Modeling, Identification and Control, Vol. 42, No.2, pp.37-46, 2021.".
The point cloud data used for training and verification is published under "Dybedal, Joacim, "Replication Data for: CNN-based People Detection in Voxel Space using Intensity Measurements and Point Cluster Flattening", https://doi.org/10.18710/HMJVFM, DataverseNO, 2021".
The repository also contains the ROS node used to filter and merge multiple XYZI Point Clouds into a single time-synchronized point cloud.
Matlab and ROS Kinetic Kame or above.
Open the matlab project and load the CNN20210131_2.mat workspace to use the pre-trained network.
Play back the 2018-08-24-095008-4human-lowsun-merged.bag rosbag file and start the "classify.m" Matlab Script.
Use RVIZ to subscribe to the "pointcloud_merger/pointcloud_out" and the "visualization_msgs/Marker" messages.
See LICENCE file.
If you use or reference this repository, please cite: "J. Dybedal and G. Hovland, CNN-based People Detection in Voxel Space using Intensity Measurements and Point Cluster Flattening. Modeling, Identification and Control, Vol. 42, No.2, pp.37-46, 2021.".
BibTeX:
@article{MIC-2021-2-1,
title={{CNN-based People Detection in Voxel Space using Intensity Measurements and Point Cluster Flattening}},
author={Dybedal, Joacim and Hovland, Geir},
journal={Modeling, Identification and Control},
volume={42},
number={2},
pages={37--46},
year={2021},
doi={10.4173/mic.2021.2.1},
publisher={Norwegian Society of Automatic Control}
}