Falling and slipping detection for pedestrians using a manifold learning approach

Sheng Bin Hsu, Chin Chuan Han, Cheng Ta Hsieh, Kuo Chin Fan

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

3 Scopus citations

Abstract

Falling activity is a critical behavior due to the physical discomfort for elders. The prime time of rescuing is missed whenever falls accidentally happen. Fall detection in real time could save human life in video surveillance systems. Recently, digital cameras are installed everywhere. Human activities are monitored from cameras by intelligent programs. An alarm is sent to the administrator when an abnormal event occurs. In this paper, a multi-view-based manifold learning algorithm is proposed for detecting the falling events. This algorithm should be able to detect people falling down in any direction. First, the walking patterns in a normal speed are modeled by the locality preserving projection (LPP). Since the duration of falling activity is hard to be estimated from real videos, partial temporal windows are matched with the normal walking patterns. The Hausdorff distances are calculated to estimate the similarity. In the experiments, the falling events are effectively detected by the proposed method.

Original languageEnglish
Title of host publicationProceedings - International Conference on Machine Learning and Cybernetics
PublisherIEEE Computer Society
Pages1189-1194
Number of pages6
ISBN (Electronic)9781479902576
DOIs
StatePublished - 2013
Event12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 - Tianjin, China
Duration: 14 Jul 201317 Jul 2013

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume3
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference12th International Conference on Machine Learning and Cybernetics, ICMLC 2013
Country/TerritoryChina
CityTianjin
Period14/07/1317/07/13

Keywords

  • Fall detection
  • Hausdorff distance
  • Locality preserving projection
  • Manifold learning

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