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
A New Approach to Fall Detection Based on the Human Torso Motion Model
TL;DR: A new approach for fall detection based on two features and their motion characteristics extracted from the human torso, which is time efficient and robust because of only calculating the changing rate of gravity and centroid height.
Proceedings ArticleDOI
Automated unusual event detection in video surveillance
TL;DR: This work presents an automatic approach for detecting and recognizing falls of elderly people in the home environments using video based technology, with a focus on the protection and assistance to the elderly people.
Proceedings ArticleDOI
Multi-Stream Deep Convolutional Network Using High-Level Features Applied to Fall Detection in Video Sequences
Sarah Almeida Cameiro,Gabriel Pellegrino da Silva,Guilherme Vieira Leite,Ricardo Moreno,Silvio Jamil Ferzoli Guimarães,Helio Pedrini +5 more
TL;DR: This work proposes and evaluates a multi-stream learning model based on convolutional neural networks using high-level handcrafted features as input in order to cope withporadic falls and shows that this approach outperforms, in terms of accuracy and sensitivity rates, to other similar tested methods found in literature.
Proceedings ArticleDOI
Real-Time Fall Detecting System Using a Tri-axial Accelerometer for Home Care
TL;DR: A home-based, real-time fall detection system that not only can distinguish up to 4 different kinds of fall events (forward, backward, rightward and leftward), but is also portable, low-cost and with high accuracy rate is proposed.
Journal ArticleDOI
Elders’ fall detection based on biomechanical features using depth camera
TL;DR: An accidental fall poses a serious threat to the health of the elderly and an increased number of surveillance systems have been installed in the elderly home to help protect against it.
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