“CNN-based People Detection in Voxel Space using Intensity Measurements and Point Cluster Flattening”

Authors: Joacim Dybedal and Geir Hovland,
Affiliation: University of Agder
Reference: 2021, Vol 42, No 2, pp. 37-46.

Keywords: Human detection, point clouds, flattening, convolutional neural network

Abstract: In this paper real-time people detection is demonstrated in a relatively large indoor industrial robot cell as well as in an outdoor environment. Six depth sensors mounted at the ceiling are used to generate a merged point cloud of the cell. The merged point cloud is segmented into clusters and flattened into gray-scale 2D images in the xy and xz planes. These images are then used as input to a classifier based on convolutional neural networks (CNNs). The final output is the 3D position (x,y,z) and bounding box representing the human. The system is able to detect and track multiple humans in real-time, both indoors and outdoors. The positional accuracy of the proposed method has been verified against several ground truth positions, and was found to be within the point-cloud voxel-size used, i.e. 0.04m. Tests on outdoor datasets yielded a detection recall of 76.9 percent and an F1 score of 0.87.

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  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},
  publisher={Norwegian Society of Automatic Control}