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Fast Human Detection for Indoor Mobile Robots Using Depth Images
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
  • Benjamin Choi, Carnegie Mellon University
  • Cetin Mericli, Carnegie Mellon University
  • Joydeep Biswas, Carnegie Mellon University
  • Manuela M. Veloso, Carnegie Mellon University
Date of Original Version
Conference Proceeding
Rights Management
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Abstract or Description
A human detection algorithm running on an indoor mobile robot has to address challenges including occlusions due to cluttered environments, changing backgrounds due to the robot's motion, and limited on-board computational resources. We introduce a fast human detection algorithm for mobile robots equipped with depth cameras. First, we segment the raw depth image using a graph-based segmentation algorithm. Next, we apply a set of parameterized heuristics to filter and merge the segmented regions to obtain a set of candidates. Finally, we compute a Histogram of Oriented Depth (HOD) descriptor for each candidate, and test for human presence with a linear SVM. We experimentally evaluate our approach on a publicly available dataset of humans in an open area as well as our own dataset of humans in a cluttered cafe environment. Our algorithm performs comparably well on a single CPU core against another HOD-based algorithm that runs on a GPU even when the number of training examples is decreased by half. We discuss the impact of the number of training examples on performance, and demonstrate that our approach is able to detect humans in different postures (e.g. standing, walking, sitting) and with occlusions.
Citation Information
Benjamin Choi, Cetin Mericli, Joydeep Biswas and Manuela M. Veloso. "Fast Human Detection for Indoor Mobile Robots Using Depth Images" Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) Vol. 2013 (2013) p. 1108 - 1113
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