Learning-based human fall detection using rgb-d cameras

Szu Hao Huang*, Ying Cheng Pan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Automatic detection of human fall events is a challenging but important function of the real-time surveillance system. The goal of the proposed system is to develop a frame-by-frame fall detection system based on real-time RGB-D camera devices. The proposed system is composed of a complex off-line learning stage which combines several novel machine learning techniques and a series of on-line detection processes. A background subtraction method based on iterative normalized-cut segmentation algorithm is proposed to identify the pixel-wise human regions rapidly. The silhouettes are extracted to measure the pose similarity between different samples. Manifold learning algorithm reduces the feature dimensions and several discriminant analysis techniques are applied to model the final human fall detector. The experimental database contains 65 color video and corresponding depth maps. The experimental results based on a leave-one-out cross-validation testing show that our proposed system can detect the fall events effectively and efficiently.

Original languageEnglish
Title of host publicationProceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013
Publisher[publishername] MVA Organization
Pages439-442
Number of pages4
ISBN (Print)9784901122139
StatePublished - 1 Jan 2013
Event13th IAPR International Conference on Machine Vision Applications, MVA 2013 - Kyoto, Japan
Duration: 20 May 201323 May 2013

Publication series

NameProceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013

Conference

Conference13th IAPR International Conference on Machine Vision Applications, MVA 2013
CountryJapan
CityKyoto
Period20/05/1323/05/13

Fingerprint Dive into the research topics of 'Learning-based human fall detection using rgb-d cameras'. Together they form a unique fingerprint.

Cite this