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A Fast Audiovisual Attention Model for Human Detection and Localization on a Companion Robot

Authors:
Rémi Ratajczak
Denis Pellerin
Catherine Garbay
Quentin Labourey

Keywords: audiovisual attention; saliency; RGB-D; human localization; companion robot

Abstract:
This paper describes a fast audiovisual attention model applied to human detection and localization on a companion robot. Its originality lies in combining static and dynamic modalities over two analysis paths in order to guide the robot’s gaze towards the most probable human beings’ locations based on the concept of saliency. Visual, depth and audio data are acquired using a RGB-D camera and two horizontal microphones. Adapted state-of-the-art methods are used to extract relevant information and fuse them together via two dimensional gaussian representations. The obtained saliency map represents human positions as the most salient areas. Experiments have shown that the proposed model can provide a mean F-measure of 66 percent with a mean precision of 77 percent for human localization using bounding box areas on 10 manually annotated videos. The corresponding algorithm is able to process 70 frames per second on the robot.

Pages: 30 to 35

Copyright: Copyright (c) IARIA, 2016

Publication date: November 13, 2016

Published in: conference

ISSN: 2519-8645

ISBN: 978-1-61208-520-3

Location: Barcelona, Spain

Dates: from November 13, 2016 to November 17, 2016