3D head tracking for fall detection using a single calibrated camera

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

doi:10.1016/j.imavis.2012.11.003

Cited by 69 publications

(35 citation statements)

References 11 publications

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“…but these systems have a pervasiveness issue: the only place where the activity is recognized is in the user's home or where the sensors are located. Another kind of research venue focuses on the usage of cameras for the recognition of gestures [20][21][22]. This is especially suitable for security (e.g.…”

Section: Activity Recognition Systems For Eldersmentioning

confidence: 99%

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Concepción

Morillo

Álvarez-García

et al. 2017

Pervasive and Mobile Computing

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a b s t r a c tCurrently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required.

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Section: Activity Recognition Systems For Eldersmentioning

confidence: 99%

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Concepción

Morillo

Álvarez-García

et al. 2017

Pervasive and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Existing methods may be broadly classified into two major categories based on their approach in exploiting either the holistic appearance [7,20,23,28] or distinct features [12,21,22,29,42] of the face for head pose estimation.…”

Section: Head Pose Estimationmentioning

confidence: 99%

“…Typical variants of appearance-based methods search for the best matching head pose from a collection of poseannotated templates [7], register a flexible model of the facial shape to target colour [28] or texture maps [23], or seek low-dimensional manifolds which model the variations in head pose robustly [20]. The achievable estimation accuracy of appearance-based methods that rely on a training stage is often contingent upon the size of the training set and the conditions under which the training data were captured [7,20,23].…”

Section: Head Pose Estimationmentioning

confidence: 99%

Model-free non-rigid head pose tracking by joint shape and pose estimation

Cristina

Camilleri

2016

Machine Vision and Applications

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Head pose estimation under non-rigid face movement is particularly useful in applications relating to eye-gaze tracking in less constrained scenarios, where the user is allowed to move naturally during tracking. Existing visionbased head pose estimation methods often require accurate initialisation and tracking of specific facial landmarks, while methods that handle non-rigid face deformations typically necessitate a preliminary training phase prior to head pose estimation. In this paper, we propose a method to estimate the head pose in real-time from the trajectories of a set of feature points spread randomly over the face region, without requiring a training phase or model-fitting of specific facial features. Conversely, our method exploits the 3-dimensional shape of the surface of interest, recovered via shape and motion factorisation, in combination with Kalman and particle filtering to determine the contribution of each feature point to the estimation of head pose based on a variance measure. Quantitative and qualitative results reveal the capability of our method in handling non-rigid face movement without deterioration of the head pose estimation accuracy.

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“…Existing methods may be broadly classified into two major categories based on their approach in exploiting either the holistic appearance [3,9,12,14] or distinct features [4,10,11,15,21] of the face for head pose estimation. Appearance-based methods generally exploit the face image information entirely to estimate the head orientation.…”

Section: Introductionmentioning

confidence: 99%

“…Appearance-based methods generally exploit the face image information entirely to estimate the head orientation. Typical variants of appearance-based methods search for the best matching head pose from a collection of pose-annotated templates [3], register a flexible model of the facial shape to target colour [14] or texture maps [12], or seek low-dimensional manifolds which model the variations in head pose robustly [9]. Feature-based methods, on the other hand, rely on a sparse set of feature points sampled at specific feature positions within the face region.…”

Section: Introductionmentioning

confidence: 99%

Model-Free Head Pose Estimation Based on Shape Factorisation and Particle Filtering

Cristina

Camilleri

2015

Computer Analysis of Images and Patterns

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Abstract. Head pose estimation is essential for several applications and is particularly required for head pose-free eye-gaze tracking where estimation of head rotation permits free head movement during tracking. While the literature is broad, the accuracy of recent vision-based head pose estimation methods is contingent upon the availability of training data or accurate initialisation and tracking of specific facial landmarks. In this paper, we propose a method to estimate the head pose in realtime from the trajectories of a set of feature points spread randomly over the face region, without requiring a training phase or model-fitting of specific facial features. Conversely, without seeking specific facial landmarks, our method exploits the sparse 3-dimensional shape of the surface of interest, recovered via shape and motion factorisation, in combination with particle filtering to correct mistracked feature points and improve upon an initial estimation of the 3-dimensional shape during tracking. In comparison with two additional methods, quantitative results obtained through our model-and landmark-free method yield a reduction in the head pose estimation error for a wide range of head rotation angles.

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3D head tracking for fall detection using a single calibrated camera

Cited by 69 publications

References 11 publications

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Model-free non-rigid head pose tracking by joint shape and pose estimation

Model-Free Head Pose Estimation Based on Shape Factorisation and Particle Filtering

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