Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations-Reference-Cited by-同舟云学术

Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations

Published:2023-06 Issue:6 Volume:8 Page:3550-3557
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Fung Angus¹^ORCID,Benhabib Beno¹^ORCID,Nejat Goldie¹^ORCID

Affiliation:

1. Autonomous Systems and Biomechatronics Lab, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

HeRo NSERC CREATE Program

AGE-WELL Inc.

Canada Research Chairs

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/10102643/10106366.pdf?arnumber=10106366

Reference51 articles.

1. Real-Time Multi-Modal People Detection and Tracking of Mobile Robots with A RGB-D Sensor*

2. RGB-D People Detection and Tracking from Small-Footprint Ground Robots

3. Deep learning-based human pose estimation: A survey;zheng,2022

4. Histograms of Oriented Gradients for Human Detection

5. Onboard robust person detection and tracking for domestic service robots;sanz;Proc Iberian Robot Conf,2015

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