Intelligent Video Surveillance for Monitoring Elderly in Home Environments

Nasution, A.H.; Emmanuel, Sabu

doi:10.1109/mmsp.2007.4412853

Cited by 89 publications

(56 citation statements)

References 5 publications

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“…Also, in both [20] and [21], authors proposed a video surveillance monitoring system to detect various body posture events. Combination of best-fit approximated ellipse around the human body, normalized horizontal and vertical projection histograms of the segmented object and temporal changes of head position, were used as the features vectors fed to a MLP Neural Network [20] and k-nearest neighbor (k-NN) algorithm [21] for motion classification and fall detection.…”

Section: Vision-based Devisesmentioning

confidence: 99%

“…Combination of best-fit approximated ellipse around the human body, normalized horizontal and vertical projection histograms of the segmented object and temporal changes of head position, were used as the features vectors fed to a MLP Neural Network [20] and k-nearest neighbor (k-NN) algorithm [21] for motion classification and fall detection. Experimental results showed a reliable recognition rate of above 90% and a stable classifier's output [21].…”

Section: Vision-based Devisesmentioning

confidence: 99%

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A Panoramic Study of Fall Detection Technologies

2016

IJCSI

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Falls are a major risk of injury for elderly aged 65 or over, blind people, people with balance disorder and leg weakness. In this regard, assistive technology which aims to identify fall events at real time can reduce the rate of impairments and mortality. This study offer a literature research reference value for bioengineers for further research. Much of the past and the current fall detection research, the vital signals features and the way features are extracted and fed to a classifier are introduced. The study concludes with an assessment of the current technologies highlighting their critical limitations along with suggestions for future research direction in this rapidly developing field of study.

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Section: Vision-based Devisesmentioning

confidence: 99%

Section: Vision-based Devisesmentioning

confidence: 99%

A Panoramic Study of Fall Detection Technologies

2016

IJCSI

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“…Nasution and Emmanuel [147] used the projection histograms of segmented human body silhouette as the main feature vector posture classification and used the speed of fall to differentiate real fall incident and an event where a person is simply lying without falling. Thome and Miguet [148] proposed a multi-view (two-camera) approach to address occlusion and used a layered HMM for motion modelling where the hierarchical architecture decoupled the motion analysis into different temporal granularity levels, which made the algorithm able to detect very sudden changes.…”

Section: Fall Detection For Elderly Peoplementioning

confidence: 99%

Human detection in surveillance videos and its applications - a review

Paul

Haque

Chakraborty

2013

EURASIP J. Adv. Signal Process.

154

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Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed. Review IntroductionOver the recent years, detecting human beings in a video scene of a surveillance system is attracting more attention due to its wide range of applications in abnormal event detection, human gait characterization, person counting in a dense crowd, person identification, gender classification, fall detection for elderly people, etc.The scenes obtained from a surveillance video are usually with low resolution. Most of the scenes captured by a static camera are with minimal change of background. Objects in the outdoor surveillance are often detected in far field. Most existing digital video surveillance systems rely on human observers for detecting specific activities in a real-time video scene. However, there are limitations in the human capability to monitor simultaneous events in surveillance displays [1]. Hence, human motion analysis in automated video surveillance has become one of the most active and attractive research topics in the area of computer vision and pattern recognition.An intelligent system detects and captures motion information of moving targets for accurate object classification. The classified object is being tracked for high-level analysis. In this study, we focus on detecting humans and do not consider recognition of their complex activities. Human detection is a difficult task from a machine vision perspective as it is influenced by a wide range of possible appearance due to changing articulated pose, clothing, lighting and background, but prior knowledge on these limitations can improve the detection performance.The detection process generally occurs in two steps: object detection and object classification. Object detection could be performed by background subtraction, optical flow and spatio-temporal filtering. Background subtraction is a popular method for object detection where it attempts to detect moving objects from the difference between the current frame and a background frame in a pixelby-pixel or block-by-block fashion. There are few available approaches to perform background subtraction. The most common ones are adaptive Gaussian mixture [2-10], non-parametric background [11][12][13][1...

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“…Video-based system for older people surveillance is growing as a research field (particularly frailty detection) [9][10][11][12][13] , as it can provide data about a people interaction with their environment (e.g., time spent in zones and interaction with objects of interest). Applications are generally associated with detection of daily living activities (e.g., eating, dressing, walking), or the detection of (potentially) dangerous situations (e.g., older people falls).…”

Section: Introductionmentioning

confidence: 99%

Alzheimers Patient Activity Assessment Using Different Sensors

Crispim-Junior¹,

Joumier²,

Hsu³

et al. 2012

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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PurposeThe number of people requiring care, including Alzheimer patients, will grow while the number of people able to provide care will decrease. We focus on the development of medical, information and communication technologies for improved diagnosis and evaluation of dementia progression in early-stage Alzheimer disease (AD) patients. Method We compared several sensors (video and accelerometer) to assess elderly performance on instrumental activities of daily living (IADL) and gait tests in the clinical protocol developed and executed by the Memory Center of the Nice Hospital and the Department of Neurology at National Cheng Kung University Hospital, Taiwan. This clinical protocol defines a set of daily living tasks (e.g., preparing coffee, watching TV), and physical tests (e.g. a balance test), that could be realistic achieved in the designed observation room, and at the same time provide objective information about dementia symptoms. Previous works analyzed only accelerometer sensors for elderly gait analysis for dementia symptoms differentiation, while video-sensors were dedicated to ADL-detection. The comparison of several sensors could provide new evidence about patients' activities. The proposed systems used a constraint-based ontology to model and detect events based on the sensors data output. 2D-video stream data is converted to 3D-geometric information that is combined with a priori semantic information about the clinical scenario. The ontology language is declarative and intuitive (as it uses natural terms), allowing medical experts to easily define and modify the IADL and gait events models (using spatial, temporal, video-tracking and accelerometers data to describe events). The sensor system has been tested with 44 participants (healthy=21, AD=23). A stride detection algorithm was developed by the Taiwan team for the automatically acquisition of gait information using a triaxial accelerometer embedded in a wearable device. It acquires data about the participant locomotion (e.g., walking time, stride length, stride frequency). It was tested with 33 participants (healthy=17, AD=16), on a 40-meter walking test. Results & Discussion This monitoring system detected the full set of activities of the first part of the clinical protocol (e.g., balance test, repeated sequence of sitting-standing positions) with a detection rate of 96.9% to 100% (true positive rate).

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Intelligent Video Surveillance for Monitoring Elderly in Home Environments

Cited by 89 publications

References 5 publications

A Panoramic Study of Fall Detection Technologies

A Panoramic Study of Fall Detection Technologies

Human detection in surveillance videos and its applications - a review

Alzheimers Patient Activity Assessment Using Different Sensors

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Intelligent Video Surveillance for Monitoring Elderly in Home Environments

Cited by 89 publications

References 5 publications

A Panoramic Study of Fall Detection Technologies

A Panoramic Study of Fall Detection Technologies

Human detection in surveillance videos and its applications - a review

Alzheimers Patient Activity Assessment Using Different Sensors

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Product

Resources

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Alzheimers Patient Activity Assessment Using Different Sensors