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2013 | OriginalPaper | Buchkapitel

10. RGBD-HuDaAct: A Color-Depth Video Database for Human Daily Activity Recognition

verfasst von : Bingbing Ni, Gang Wang, Pierre Moulin

Erschienen in: Consumer Depth Cameras for Computer Vision

Verlag: Springer London

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Abstract

In this chapter, we present a home-monitoring oriented human activity recognition benchmark database, based on the combination of a color video camera and a depth sensor. Our contributions are two-fold: (1) We have created a human activity video database named RGBD-HuDaAct, which contains synchronized color-depth video streams, for the task of human daily activity recognition. This database aims at encouraging research in human activity recognition based on multi-modal video data (color plus depth). (2) We have designed two multi-modality fusion schemes which naturally combine color and depth information from two state-of-the-art feature representation methods for action recognition, namely, spatio-temporal interest points (STIPs) and motion history images (MHIs). These depth-extended feature representation methods are evaluated comprehensively, and superior recognition performance related to their uni-modal (color only) counterparts is demonstrated.

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Vorheriges Kapitel RGB-D Object Recognition: Features, Algorithms, and a Large Scale Benchmark

http://nicolas.burrus.name/index.php/research/kinectcalibration

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Titel: RGBD-HuDaAct: A Color-Depth Video Database for Human Daily Activity Recognition
verfasst von: Bingbing Ni
Gang Wang
Pierre Moulin
Verlag: Springer London
Buch: Consumer Depth Cameras for Computer Vision
Print ISBN: 978-1-4471-4639-1

Electronic ISBN: 978-1-4471-4640-7

Copyright-Jahr: 2013
DOI: https://doi.org/10.1007/978-1-4471-4640-7_10

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