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This paper describes the development and testing of an Ambient Assisted Living (AAL) solution that combines state-of-the- art sensor technology with a social network application to empower elders to stay active, autonomous and socially connected and consequently support and unburden family caregivers. From a very early development phase both social scientists and engineers worked together to ensure a holistic approach to the development of the technology. To get a better insight into the needs, wishes and requirements of potential user groups, a qualitative approach was used to collect data among elderly and their caregivers from three European countries, namely Belgium, France and the United Kingdom. For the development of the technology, very low-resolution (900-pixel) visual sensors and Passive InfraRed (PIR) sensors were used and installed in a couple of homes of elderly to enable the monitoring and the analysis of the in-home activities of the elderly. In our application, automated analysis of both in-home activities and social network activities are fused to recommend new social connections and personalized activities to the elderly in order to stimulate social and physical activity. Additionally, algorithms for behavioral change analysis on the sensor data follow the health status of the elderly, such as the detection of disturbed sleep and eat patterns. From the qualitative studies interesting findings arose such as elderly mentioning safety and mobility benefits and the increased peace of mind while privacy issues, the lack of human touch and an unfelt need for support formed potential barriers towards adoption of AAL technologies.

In this paper, we propose a novel extrinsic calibration method for camera networks by analyzing tracks of pedestrians. First of all, we extract the center lines of walking persons by detecting their heads and feet in the camera images. We propose an easy and accurate method to estimate the 3D positions of the head and feet w.r.t. a local camera coordinate system from these center lines. We also propose a RANSAC-based orthogonal Procrustes approach to compute relative extrinsic parameters connecting the coordinate systems of cameras in a pairwise fashion. Finally, we refine the extrinsic calibration matrices using a method that minimizes the reprojection error. While existing state-of-the-art calibration methods explore epipolar geometry and use image positions directly, the proposed method first computes 3D positions per camera and then fuses the data. This results in simpler computations and a more flexible and accurate calibration method. Another advantage of our method is that it can also handle the case of persons walking along straight lines, which cannot be handled by most of the existing state-of-the-art calibration methods since all head and feet positions are co-planar. This situation often happens in real life.

Copyright © 2015 SCITEPRESS - Science and Technology Publications All rights reserved. Analysing human sports activity in computer vision requires reliable segmentation of the human body into meaningful parts, such as arms, torso and legs. Therefore, we present a novel strategy for human body segmentation. Firstly, greyscale images of human bodies are divided into smooth intensity patches with an adaptive region growing algorithm based on low-degree polynomial fitting. Then, the key idea in this paper is that human body parts are approximated by nearly cylindrical surfaces, of which the axes of minimum curvature accurately reconstruct the human body skeleton. Next, human body segmentation is qualitatively evaluated with a line segment distance between reconstructed human body skeletons and ground truth skeletons. When compared with human body parts segmentations based on mean shift, normalized cuts and watersheds, the proposed method achieves more accurate segmentations and better reconstructions of human body skeletons. Deboeverie F., De Geest R., Tuytelaars T., Veelaert P., Philips W., ''Curvature-based human body parts segmentation in physiotherapy'', Proceedings 10th international conference on computer vision theory and applications - VISAPP 2015, pp. 630-637, March 11-14, 2015, Berlin, Germany. ispartof: pages:630-637 ispartof: Proceedings VISAPP 2015 vol:1 pages:630-637 ispartof: International conference on computer vision theory and applications - VISAPP 2015 location:Berlin, Germany date:11 Mar - 14 Mar 2015 status: published

This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 × 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics.

This paper proposes an automatic and real-time system for face analysis, usable in visual communication applications. In this approach, faces are represented with Curve Edge Maps, which are collections of polynomial segments with a convex region. The segments are extracted from edge pixels using an adaptive incremental linear-time fitting algorithm, which is based on constructive polynomial fitting. The face analysis system considers face tracking, face recognition and facial feature detection, using Curve Edge Maps driven by histograms of intensities and histograms of relative positions. When applied to different face databases and video sequences, the average face recognition rate is 95.51%, the average facial feature detection rate is 91.92% and the accuracy in location of the facial features is 2.18% in terms of the size of the face, which is comparable with or better than the results in literature. However, our method has the advantages of simplicity, real-time performance and extensibility to the different aspects of face analysis, such as recognition of facial expressions and talking.