ML Anthology

Lightweight Monocular Obstacle Avoidance by Salient Feature Fusion

Andrea Manno-Kovacs, Levente Kovács
ICCVW 2017 pp. 734-741
doi:10.1109/ICCVW.2017.92 /iccvw/2017/mannokovacs2017iccvw-lightweight/

Abstract

We present a monocular obstacle avoidance method based on a novel image feature map built by fusing robust saliency features, to be used in embedded systems on lightweight autonomous vehicles. The fused salient features are a textural-directional Harris based feature map and a relative focus feature map. We present the generation of the fused salient map, along with its application for obstacle avoidance. Evaluations are performed from a saliency point of view, and for the assessment of the method's applicability for obstacle avoidance in simulated environments. The presented results support the usability of the method in embedded systems on lightweight unmanned vehicles.

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Cite

Text

Manno-Kovacs and Kovács. "Lightweight Monocular Obstacle Avoidance by Salient Feature Fusion." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.92

Markdown

[Manno-Kovacs and Kovács. "Lightweight Monocular Obstacle Avoidance by Salient Feature Fusion." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/mannokovacs2017iccvw-lightweight/) doi:10.1109/ICCVW.2017.92

BibTeX

@inproceedings{mannokovacs2017iccvw-lightweight,
  title     = {{Lightweight Monocular Obstacle Avoidance by Salient Feature Fusion}},
  author    = {Manno-Kovacs, Andrea and Kovács, Levente},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2017},
  pages     = {734-741},
  doi       = {10.1109/ICCVW.2017.92},
  url       = {https://mlanthology.org/iccvw/2017/mannokovacs2017iccvw-lightweight/}
}