DocumentCode :
2426645
Title :
Robust Fall Detection Using Human Shape and Multi-class Support Vector Machine
Author :
Foroughi, Homa ; Rezvanian, Alireza ; Paziraee, Amirhossien
Author_Institution :
Dept. of Comput. Eng., Ferdowsi Univ. of Mashhad, Mashhad
fYear :
2008
fDate :
16-19 Dec. 2008
Firstpage :
413
Lastpage :
420
Abstract :
Falls and resulting physical-psychological consequences in the elderly are a major health hazard and a serious obstacle for independent living. So development of intelligent video surveillance systems is so important due to providing safe and secure environments. To this end, this paper proposes a novel approach for human fall detection based on human shape variation. Combination of best-fit approximated ellipse around the human body, projection histograms of the segmented silhouette and temporal changes of head pose, would provide a useful cue for detection different behaviors. Extracted feature vectors are finally fed to a multi-class support vector machine for precise classification of motions and determination of a fall event. Unlike existent fall detection systems that only deal with limited movement patterns, we considered wide range of motions consisting of normal daily life activities, abnormal behaviors and also unusual events. Reliable recognition rate of experimental results underlines satisfactory performance of our system.
Keywords :
feature extraction; geriatrics; health hazards; image classification; image motion analysis; image segmentation; object detection; pose estimation; shape recognition; support vector machines; video surveillance; behavior detection; elderly; feature extraction; head pose; health hazard; human fall detection; human shape variation; intelligent video surveillance system; motion classification; multiclass support vector machine; physical-psychological consequence; projection histogram; robust fall detection; segmented silhouette; Hazards; Histograms; Humans; Intelligent systems; Machine intelligence; Robustness; Senior citizens; Shape; Support vector machines; Video surveillance; Human Fall Detection; Human Shape Variation; Multi-class Support Vector Machine; Posture Recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision, Graphics & Image Processing, 2008. ICVGIP '08. Sixth Indian Conference on
Conference_Location :
Bhubaneswar
Print_ISBN :
978-0-7695-3476-3
Electronic_ISBN :
978-0-7695-3476-3
Type :
conf
DOI :
10.1109/ICVGIP.2008.49
Filename :
4756100
Link To Document :
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