Monitoring Activities of Daily Living (ADLs) of Elderly Based on 3D Key Human Postures

Zouba, Nadia; Boulay, Bernard; Brémond, François; Thonnat, Monique

doi:10.1007/978-3-540-92781-5_4

Cited by 40 publications

(25 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The laboratory is located in the CSTB (Centre Scientifique de Techniques du Batiment) at Sophia Antipolis. The Laboratory promotes research in the domain of activity monitoring and assisted living (Zouba et al, 2008). In Fig.…”

Section: Resultsmentioning

confidence: 99%

Fusion of Motion Segmentation With Online Adaptive Neural Classifier for Robust Tracking

Bąk¹,

Sundaram²,

Brémond³

et al. 2009

Proceedings of the Fourth International Conference on Computer Vision Theory and Applications

View full text Add to dashboard Cite

Abstract:This paper presents a method to fuse the information from motion segmentation with online adaptive neural classifier for robust object tracking. The motion segmentation with object classification identify new objects present in the video sequence. This information is used to initialize the online adaptive neural classifier which is learned to differentiate the object from its local background. The neural classifier can adapt to illumination variations and changes in appearance. Initialized objects are tracked in following frames using the fusion of their neural classifiers with the feedback from the motion segmentation. Fusion is used to avoid drifting problems due to similar appearance in the local background region. We demonstrate the approach in several experiments using benchmark video sequences with different level of complexity.

show abstract

Section: Resultsmentioning

confidence: 99%

Fusion of Motion Segmentation With Online Adaptive Neural Classifier for Robust Tracking

Bąk¹,

Sundaram²,

Brémond³

et al. 2009

Proceedings of the Fourth International Conference on Computer Vision Theory and Applications

View full text Add to dashboard Cite

show abstract

“…The whole workflow runs in real time (4 to 15 fps). Further work [134], applies the described technique in an AAL scenario. This way, there are works which choose to cover the blob with an elliptical model in order to extract conclusions based on the direction of the major and minor axis of the ellipse and the length ratio between them (see [74]).…”

Section: Pose Estimationmentioning

confidence: 99%

“…With such a scheme, the MeMoMan project [7] has been able to determine very fine pose estimation in real time. The technique is similar to [13,134]. Using the silhouettes (in this case three), a hierarchical 3D human model is applied, which fits into the observed silhouettes.…”

Section: Pose Estimationmentioning

confidence: 99%

A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living

Chaaraoui

Climent-Pérez

Flórez-Revuelta

2012

Expert Systems with Applications

182

122

View full text Add to dashboard Cite

Human Behaviour Analysis (HBA) is more and more being of interest for Computer Vision and Artificial Intelligence researchers. Its main application areas, like Video Surveillance and Ambient-Assisted Living (AAL), have been in great demand in recent years. This paper provides a review on HBA for AAL and ageing in place purposes focusing specially on vision techniques. First, a clearly defined taxonomy is presented in order to classify the reviewed works, which are consequently presented following a bottom-up abstraction and complexity order. At the motion level, pose and gaze estimation as well as basic human movement recognition are covered. Next, the mainly used action and activity recognition approaches are presented with examples of recent research works. Increasing the degree of semantics and the time interval involved in the HBA, finally the behaviour level is reached. Furthermore, useful tools and datasets are analysed in order to provide help for initiating projects.

show abstract

“…GERHOME Video Sequence: Now, we present results from the video sequence for independent living of elderly people at home (GERHOME) [33]. In this sequence, the mobile object 'elderly person' experiences change in appearance and partial occlusion with background.…”

Section: Handling Partial Occlusionsmentioning

confidence: 99%

Online learning neural tracker

et al. 2011

View full text Add to dashboard Cite

Object tracking is a fundamental computer vision problem and is required for many high-level tasks such as activity recognition, behavior analysis and surveillance. The main challenge in the object tracking problem is the dynamic change in object/background appearance, illumination, shape and occlusion. We present an online learning neural tracker (OLNT) to differentiate the object from the background and also adapt to changes in object/background dynamics. In the proposed tracking system, we propose a new mobile object detection module which identifies new mobile objects in the scene and then OLNT faithfully locates them in subsequent frames. The OLNT extracts region-based features like region-based color moments for larger mobile objects and color and texture features at pixel level for smaller mobile objects.For target modeling and object tracking, new neural classifier algorithm based on risk sensitive loss function is proposed to handle issues related to sample imbalance and change in characteristic of object class in future frames. The proposed neural classifier automatically determines the number of neurons required to estimate the posterior probability map. In the online learning neural classifier, only one neuron parameter is updated per tracker to reduce the computational burden during online adaptation.The tracked object is represented using an estimated posterior probability map. The signature of the posterior probability map is used to adapt the bounding box to handle the scale change and improper initialization.For illustrating the advantage of the proposed OLNT under rapid illumination variation, change in appearance, scale/size change, and occlusion, we present results from benchmark video sequences. The results clearly highlight the advantages of the proposed OLNT. Finally, we also present the comparison with well-known ensemble tracker in one the sequence and highlight the advantage of the proposed tracker. Index TermsObject tracking, neural classifier, Gaussian activation function, posterior probability map, signature in 2D, risk sensitive hinge loss function.

show abstract

Monitoring Activities of Daily Living (ADLs) of Elderly Based on 3D Key Human Postures

Cited by 40 publications

References 19 publications

Fusion of Motion Segmentation With Online Adaptive Neural Classifier for Robust Tracking

Fusion of Motion Segmentation With Online Adaptive Neural Classifier for Robust Tracking

A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living

Online learning neural tracker

Contact Info

Product

Resources

About