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
Fall Detection in Videos With Trajectory-Weighted Deep-Convolutional Rank-Pooling Descriptor
TL;DR: Wang et al. as discussed by the authors presented trajectory-weighted deep-convolutional rank-pooling descriptor (TDRD) for fall detection, which is robust to surrounding environments and can describe the dynamics of human actions in long time videos effectively.
Proceedings ArticleDOI
Human fall detection based on adaptive background mixture model and HMM
Khue Tra,Tuan Van Pham +1 more
TL;DR: The qualitative results demonstrate that the combination of the adaptive GMM-based object segmentation and HMM certainly improves recognition accuracy under different scenarios.
Journal ArticleDOI
3D measures exploitation for a monocular semi-supervised fall detection system
Konstantinos Makantasis,Eftychios Protopapadakis,Anastasios Doulamis,Nikolaos Doulamis,Nikolaos F. Matsatsinis +4 more
TL;DR: A system is presented, which tries to address the fall detection problem through visual cues, which utilizes a fast, real-time background subtraction algorithm, based on motion information in the scene and pixels intensity, capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object.
Proceedings ArticleDOI
Fall detection using history triple features
TL;DR: The capability of the History Triple Features technique, based on the Trace transform, to provide noise robust and invariant to different variations features for the spatiotemporal representation of fall occurrences is examined.
Journal ArticleDOI
Collaborative Fall Detection Using Smart Phone and Kinect
TL;DR: A collaborative detection platform that combines two subsystems that detects falls by fusing the data of both subsystems using two methods: the logical rules-based and D-S evidence fusion theory-based methods.
References
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Journal ArticleDOI
A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Journal ArticleDOI
The Hungarian method for the assignment problem
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, and it may be of some interest to tell the story of its origin this article.
Journal ArticleDOI
Shape matching and object recognition using shape contexts
TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
Journal ArticleDOI
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.
The Hungarian Method for the Assignment Problem.
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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