A Region-based Fusion Scheme for Human Detection in Autonomous Navigation Applications

@article{Barmpoutis2019ARF,
  title={A Region-based Fusion Scheme for Human Detection in Autonomous Navigation Applications},
  author={Panagiotis Barmpoutis and Tania Stathaki and Mar{\'i}a Irene Ruiz Gonz{\'a}lez},
  journal={IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society},
  year={2019},
  volume={1},
  pages={5566-5571}
}
Human and object detection is a continuously explored subject in tracking and navigation applications as well as within the machine vision community. More precisely, in navigation applications that are designed for robotics purposes or in order to support car drivers, the real-time detection of presence of humans and other objects is an important and challenging task. Specifically, human and object detection is a significant part of a human-computer collaboration in the sense of allowing… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 33 REFERENCES

You Only Look Once: Unified, Real-Time Object Detection

VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL

The Fastest Deformable Part Model for Object Detection

VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Fast Feature Pyramids for Object Detection

VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Histograms of oriented gradients for human detection

  • Navneet Dalal, Bill Triggs
  • Computer Science
  • 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
  • 2005
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

VIEW 1 EXCERPT

A real-time deep learning pedestrian detector for robot navigation

  • D. Ribeiro, A. Mateus, +3 authors April
  • 2017
VIEW 1 EXCERPT