Percentage of human-occupied areas for fall detection from two views

Mousse, Mikaël A.; Motamed, Cina; Ezin, Eugène C.

doi:10.1007/s00371-016-1296-y

Cited by 27 publications

(12 citation statements)

References 28 publications

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“…By comparing algorithms we conclude that our algorithm has similar performance to recent algorithms. Such as Hung and Saito 28 and Mousse et al 23 algorithms, our method only fails in the 22nd scenario in which the person is sitting on a chair and suddenly slips to the floor. In addition of fall detection, our system recognizes the other postures.…”

Section: Performance Evaluationmentioning

confidence: 96%

“…Auvinet et al 21 presents the GPU implementation to realize their method in real time. Meanwhile, such as Hung and Saito 28 and Mousse et al 23 methods, our method composing of low-cost modules is implemented in real-time in a common desktop PC and achieves very competitive performance. Then we compare our processing time to the processing times Hung and Saito 28 and Mousse et al 23 This comparison is performed in Table 4 and the values are expressed in frames per second.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Meanwhile, such as Hung and Saito 28 and Mousse et al 23 methods, our method composing of low-cost modules is implemented in real-time in a common desktop PC and achieves very competitive performance. Then we compare our processing time to the processing times Hung and Saito 28 and Mousse et al 23 This comparison is performed in Table 4 and the values are expressed in frames per second. According to this table, we conclude that our algorithm is of lower computational cost than the algorithms proposed by Hung and Saito 28 and Mousse et al 23…”

Section: Performance Evaluationmentioning

confidence: 99%

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Saliency based human fall detection in smart home environments using posture recognition

Mousse

Atohoun²

2021

Health Informatics J

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The implementation of people monitoring system is an evolving research theme. This paper introduces an elderly monitoring system that recognizes human posture from overlapping cameras for people fall detection in a smart home environment. In these environments, the zone of movement is limited. Our approach used this characteristic to recognize human posture fastly by proposing a region-wise modelling approach. It classifies persons pose in four groups: standing, crouching, sitting and lying on the floor. These postures are obtained by calculating an estimation of the human bounding volume. This volume is estimated by obtaining the height of the person and its surface that is in contact with the ground according to the foreground information of each camera. Using them, we distinguish each postures and differentiate lying on floor posture, which can be considered as the falling posture from other postures. The global multiview information of the scene is obtaining by using homographic projection. We test our proposed algorithm on multiple cameras based fall detection public dataset and the results prove the efficiency of our method.

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Section: Performance Evaluationmentioning

confidence: 96%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Saliency based human fall detection in smart home environments using posture recognition

Mousse

Atohoun²

2021

Health Informatics J

Self Cite

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“…Liao et al 15 applied a Bayesian belief network model to pedestrian characteristics to detect slips and falls. 3-D images: Two or more cameras are used to capture 3-D images for fall detection in an ideal hardware configuration. 16,17 Depth images: Depth information for each pixel in a 2-D image is used to produce a 3-D image. Structured light cameras are used to obtain image depth, including Microsoft’s Kinect and time-of-flight cameras. …”

Section: Introductionmentioning

confidence: 99%

“…3-D images: Two or more cameras are used to capture 3-D images for fall detection in an ideal hardware configuration. 16,17 3. Depth images: Depth information for each pixel in a 2-D image is used to produce a 3-D image.…”

Section: Introductionmentioning

confidence: 99%

Fall detection for multiple pedestrians using a PCA approach to 3-D inclination

Lin

2019

International Journal of Engineering Business Management

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Aging populations in developing countries have driven increased research interest in issues related to “eldercare,” with a particular focus on early fall detection. This article describes an approach to processing images captured using a depth camera to predict the probable inclination of an imminent fall by a pedestrian. First, the 3-D object reconstruction method is explained and realized. Then, the 3-D object inclination analysis is given. Experimental results show prediction rates achieved accuracy up to 98.11%, making this approach promising for use in applications in hospital and home environments in the near future.

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