Fall Detection from Human Shape and Motion History Using Video Surveillance

Rougier, C.; Meunier, Jean; St-Arnaud, Alain; Rousseau, Jacqueline

doi:10.1109/ainaw.2007.181

Cited by 270 publications

(148 citation statements)

References 8 publications

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“…The formula to calculate C motion is shown as follow [7] C motion = pixel(x,y)∈blob H τ (x, y, t) pixels ∈ blob (2) where blob represents the region of the person extracted using the code-book background subtraction, and H τ (x, y, t)…”

Section: Movement Classificationmentioning

confidence: 99%

Fall detection in the elderly by head tracking

Naqvi

Chambers

2009

2009 IEEE/SP 15th Workshop on Statistical Signal Processing

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In the paper, we propose a fall detection method based on head tracking within a smart home environment equipped with video cameras. A motion history image and code-book background subtraction are combined to determine whether large movement occurs within the scene. Based on the magnitude of the movement information, particle filters with different state models are used to track the head. The head tracking procedure is performed in two video streams taken by two separate cameras and three-dimensional head position is calculated based on the tracking results. Finally, the threedimensional horizontal and vertical velocities of the head are used to detect the occurrence of a fall. The success of the method is confirmed on real video sequences.

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Section: Movement Classificationmentioning

confidence: 99%

Fall detection in the elderly by head tracking

Naqvi

Chambers

2009

2009 IEEE/SP 15th Workshop on Statistical Signal Processing

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“…Instead of using 2-d features (as in [5], [6], [8] and [7]), we use 3-d features derived from various view angles of multiple cameras in order to reliably detect falls in different directions.…”

mentioning

confidence: 99%

One class boundary method classifiers for application in a video-based fall detection system

Naqvi

Rhuma

et al. 2012

IET Comput. Vis.

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In this paper, we introduce a video-based robust fall detection system for monitoring an elderly person in a smart room environment. Video features, namely the centroid and orientation of a voxel person, are extracted. The boundary method, which is an example one class classification technique, is then used to determine whether the incoming features lie in the 'fall region' of the feature space, and thereby effectively distinguishing a fall from other activities, such as walking, sitting, standing, crouching or lying. Four different types of boundary methods, k-center, k-th nearest neighbor, one class support vector machine and single class minimax probability machine are assessed on representative test datasets. The comparison is made on the following three aspects: 1). True positive rate, false positive rate and geometric means in detection 2). Robustness to noise in the training dataset 3). The computational time for the test phase. From the comparison results, we show that the single class minimax probability machine achieves the best overall performance. By applying one class classification techniques with 3-d features, we can obtain a more efficient fall detection system with acceptable performance, as shown in the experimental part; besides, it can avoid the drawbacks of other traditional fall detection methods.

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“…Furthermore, over 30% of adults over 65 fall at least once a year [9], making them the most common cause of injury death [10] with a direct cost of $30 billion [11]. Although the vast majority of fall detection systems use solely cameras [12,13], some systems use a combination of sensors [14].…”

Section: Introductionmentioning

confidence: 99%

Multi-modal Person Localization And Emergency Detection Using The Kinect

Galatas¹,

Ferdous²,

Makedon³

2013

IJARAI

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Abstract-Person localization is of paramount importance in an ambient intelligence environment since it is the first step towards context-awareness. In this work, we present the development of a novel system for multi-modal person localization and emergency detection in an assistive ambient intelligence environment for the elderly. Our system is based on the depth sensor and microphone array of 2 Kinect devices. We use skeletal tracking conducted on the depth images and sound source localization conducted on the captured audio signal to estimate the location of a person. In conjunction with the location information, automatic speech recognition is used as a natural and intuitive means of communication in order to detect emergencies and accidents, such as falls. Our system attained high accuracy for both the localization and speech recognition tasks, verifying its effectiveness.

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Fall Detection from Human Shape and Motion History Using Video Surveillance

Cited by 270 publications

References 8 publications

Fall detection in the elderly by head tracking

Fall detection in the elderly by head tracking

One class boundary method classifiers for application in a video-based fall detection system

Multi-modal Person Localization And Emergency Detection Using The Kinect

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