3D Head Trajectory Using a Single Camera

Rougier, C.; Meunier, Jean

doi:10.1007/978-3-642-13681-8_59

Cited by 14 publications

(10 citation statements)

References 15 publications

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“…(2) If the training dataset is large enough, the well-trained classifier can effectively distinguish different types of postures, which are used for fall detection. And this procedure (including parameter optimization and classifier training) is totally automatic and there is no need for us to set some thresholds like the threshold-based methods in [7], [8] and [9]. (3) Because the posture classification based method is performed at the frame level, there is no need for a video segment which is necessary in the video clip based fall detection methods such as [14] and [15].…”

Section: Discussionmentioning

confidence: 99%

“…In these two papers, the head's velocity information and the shape change information were extracted and appropriate thresholds were set manually to differentiate fall and non-fall activities. However these two methods produce high false detection rates (such as when a fast sitting activity was misclassified as a fall activity [7]) and the performance was strongly related to the set threshold. Another threshold based method was proposed in [9] in which calibrated cameras were used to reconstruct the three-dimensional shape of people.…”

Section: ) Computer Vision Based Methodsmentioning

confidence: 99%

“…For computer vision based fall detection methods, some researcher- s have extracted information from the captured video and a simple threshold method has been applied to determine whether there is a fall or not; representative ones due to Rougier et al are [7] and [8]. In these two papers, the head's velocity information and the shape change information were extracted and appropriate thresholds were set manually to differentiate fall and non-fall activities.…”

Section: ) Computer Vision Based Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

Rhuma

Naqvi

et al. 2012

IEEE Trans. Inform. Technol. Biomed.

263

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Abstract-We propose a novel computer vision based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine (DAGSVM) for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.

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Section: Discussionmentioning

confidence: 99%

Section: ) Computer Vision Based Methodsmentioning

confidence: 99%

Section: ) Computer Vision Based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

Rhuma

Naqvi

et al. 2012

IEEE Trans. Inform. Technol. Biomed.

263

View full text Add to dashboard Cite

show abstract

“…In [14] and [15], the head's velocity information and the shape change information were extracted from video recording and appropriate thresholds were set manually to differentiate fall and non-fall activities. However these two methods produce high false detection rates (such as when a fast sitting activity was misclassified as a fall activity in [14]). In [2], multiple calibrated cameras were used to reconstruct the three-dimensional shape of people.…”

Section: Unpublished Working Draft Not For Distributionmentioning

confidence: 99%

Computer vision based fall detection by a convolutional neural network

Gong

Kollias

2017

Proceedings of the 19th ACM International Conference on Multimodal Interaction

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In this work, we propose a novel computer vision based fall detection system, which could be applied for the health-care of the elderly people community. For a recorded video stream, background subtraction is firstly applied to extract the human body silhouette. Extracted silhouettes corresponding to daily activities are applied to construct a convolutional neural network, which is applied for classification of different classes of human postures (e.g., bend, stand, lie and sit) and detection of a fall event (i.e., lying posture is detected in the floor region). As far as we know, this work is the first attempt for the application of the convolutional neural network for the fall detection application. From a dataset of daily activities recorded from multiple people, we show that the proposed method both achieves higher postures classification results than the state-of-the-art classifiers and can successfully detect the fall event with a low false alarm rate.

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“…Some authors build a blob around the person [5]. Others track an element of the person's body as the head [6], the head and the feet [7] or human centroid [8]. Finally, the methods to detect falls may be based on the detection of a person near the ground [8], the vertical speed [9] or the scene training of inactivity places [10].…”

Section: Proposed Approachmentioning

confidence: 99%

Automatic Fall Detection System with a RGB-D Camera using a Hidden Markov Model

Dubois

Charpillet

2013

Lecture Notes in Computer Science

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Abstract. Falls in the elderly is a major public health problem because of their frequency and their medical and social consequences. New smart assistive technologies and Health Telematics make it possible to provide elderly with more security and well being at home. A smart home can automatically monitor home activities for early warning in health changes or detecting dangerous situations. One of our objectives is to design an automatic system to detect fall at home, which in its final version will be made up of a network of RGB-D sensors. In this paper, we present a simple and robust method based on the identification and tracking of the center of mass of people evolving in an indoor environment. Using a simple Hidden Markov Model whose observations are the position of the center of mass, its velocity and the general shape of the body, we can surprisingly monitor the activity of a person with high accuracy and thus detect falls with very good accuracy without false positives. An experimental study, that is reported here, has been driven in our smart apartment lab. 26 subjects were asked to perform a predefined scenario in which they realized a set of eight postures. 2 hours of video (216 000 frames) were recorded for the evaluation, half of it being used for the training of the model. The system detected the falls without false positives. This result encourages us to use this system in real situation for a better study of its efficiency.

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3D Head Trajectory Using a Single Camera

Cited by 14 publications

References 15 publications

A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

Computer vision based fall detection by a convolutional neural network

Automatic Fall Detection System with a RGB-D Camera using a Hidden Markov Model

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