Journal ArticleDOI
Robust Video Surveillance for Fall Detection Based on Human Shape Deformation
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TLDR
A new method is proposed to detect falls by analyzing human shape deformation during a video sequence, which gives very good results (as low as 0% error with a multi-camera setup) compared with other common image processing methods.Abstract:
Faced with the growing population of seniors, developed countries need to establish new healthcare systems to ensure the safety of elderly people at home. Computer vision provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. In this paper, a new method is proposed to detect falls by analyzing human shape deformation during a video sequence. A shape matching technique is used to track the person's silhouette along the video sequence. The shape deformation is then quantified from these silhouettes based on shape analysis methods. Finally, falls are detected from normal activities using a Gaussian mixture model. This paper has been conducted on a realistic data set of daily activities and simulated falls, and gives very good results (as low as 0% error with a multi-camera setup) compared with other common image processing methods.read more
Citations
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Journal ArticleDOI
From Fall Detection to Fall Prevention: A Generic Classification of Fall-Related Systems
TL;DR: This paper is to give a comprehensive overview on elderly falls and to propose a generic classification of fall-related systems based on their sensor deployment and data processing techniques in both fall detection and fall prevention tracks.
Journal ArticleDOI
A review of state-of-the-art techniques for abnormal human activity recognition
TL;DR: The proposed literature provides feature designs of abnormal human activity recognition in a video with respect to the context or application such as fall detection, Ambient Assistive Living, homeland security, surveillance or crowd analysis using RGB, depth and skeletal evidence.
Proceedings ArticleDOI
A survey on vision-based fall detection
TL;DR: This article is a survey of systems and algorithms which aim at automatically detecting cases where a human falls and may have been injured and focuses on vision-based methods.
Journal ArticleDOI
An Online One Class Support Vector Machine-Based Person-Specific Fall Detection System for Monitoring an Elderly Individual in a Room Environment
TL;DR: From the comprehensive experimental evaluations on datasets for 12 people, it is confirmed that the proposed person-specific fall detection system can achieve excellent fall detection performance with 100% fall detection rate and only 3% false detection rate with the optimally tuned parameters.
Journal ArticleDOI
A simple vision-based fall detection technique for indoor video surveillance
TL;DR: A new vision-based fall detection technique that is based on human shape variation where only three points are used to represent a person instead of the conventional ellipse or bounding box, which increases the fall detection rate without increasing the computational complexity.
References
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A Survey of Outlier Detection Methodologies
Victoria J. Hodge,Jim Austin +1 more
TL;DR: A survey of contemporary techniques for outlier detection is introduced and their respective motivations are identified and distinguish their advantages and disadvantages in a comparative review.
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TL;DR: This paper has always been one of my favorite “children,” combining as it does elements of the duality of linear programming and combinatorial tools from graph theory.
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