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In this paper, we propose a novel extrinsic calibration method for camera networks using a sphere as the calibration object. First of all, we propose an easy and accurate method to estimate the 3D positions of the sphere center w.r.t. the local camera coordinate system. Then, we propose to use orthogonal procrustes analysis to pairwise estimate the initial camera relative extrinsic parameters based on the aforementioned estimation of 3D positions. Finally, an optimization routine is applied to jointly refine the extrinsic parameters for all cameras. Compared to existing sphere-based 3D position estimators which need to trace and analyse the outline of the sphere projection in the image, the proposed method requires only very simple image processing: estimating the area and the center of mass of the sphere projection. Our results demonstrate that we can get a more accurate estimate of the extrinsic parameters compared to other sphere-based methods. While existing state-of-the-art calibration methods use point like features and epipolar geometry, the proposed method uses the sphere-based 3D position estimate. This results in simpler computations and a more flexible and accurate calibration method. Experimental results show that the proposed approach is accurate, robust, flexible and easy to use.

In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods.

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.

This paper presents a new method to track multiple people reliably using a network of calibrated smart cameras. The task of tracking multiple persons is very difficult due to non-rigid nature of the human body, occlusions and environmental changes. Our proposed method recursively updates the positions of all persons based on the observed foreground images from all smart cameras and the previously known location of each person. The performance of our proposed method is evaluated on indoor video sequences containing person– person/object–person occlusions and sudden illumination changes. The results show that our method performs well with Multiple Object Tracking Accuracy as high as 100% and Multiple Object Tracking Precision as high as 86%. Performance comparison to a state of the art tracking system shows that our method outperforms.

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.

Loneliness is a common condition associated with aging and comes with extreme health consequences including decline in physical and mental health, increased mortality and poor living conditions. Detecting and assisting lonely persons is therefore important-especially in the home environment. The current studies analyse the Activities of Daily Living (ADL) usually with the focus on persons living alone, e.g., to detect health deterioration. However, this type of data analysis relies on the assumption of a single person being analysed, and the ADL data analysis becomes less reliable without assessing socialization in seniors for health state assessment and intervention. In this paper, we propose a network of cheap low-resolution visual sensors for the detection of visitors. The visitor analysis starts by visual feature extraction based on foreground/background detection and morphological operations to track the motion patterns in each visual sensor. Then, we utilize the features of the visual sensors to build a Hidden Markov Model (HMM) for the actual detection. Finally, a rule-based classifier is used to compute the number and the duration of visits. We evaluate our framework on a real-life dataset of ten months. The results show a promising visit detection performance when compared to ground truth.

© Springer International Publishing Switzerland 2015. Object recognition in video is in most cases solved by extracting keyframes from the video and then applying still image recognition methods on these keyframes only. This procedure largely ignores the temporal dimension. Nevertheless, the way an object moves may hold valuable information on its class. Therefore, in this work, we analyze the effectiveness of different motion descriptors, originally developed for action recognition, in the context of action-invariant object recognition. We conclude that a higher classification accuracy can be obtained when motion descriptors (specifically, HOG and MBH around trajectories) are used in combination with standard static descriptors extracted from keyframes. Since currently no suitable dataset for this problem exists, we introduce two new datasets and make them publicly available. De Geest R., Deboeverie F., Philips W., Tuytelaars T., ''Spatio-temporal object recognition'', Advanced concepts for intelligent vision systems - Acivs 2015, 12 pp., October 26-29, 2015, Catania, Italy. ispartof: pages:681-692 ispartof: ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, ACIVS 2015 vol:9386 pages:681-692 ispartof: Advanced concepts for intelligent vision systems - Acivs 2015 location:Catania, Italy date:26 Oct - 29 Oct 2015 status: published

Nearly half of the senior citizens report difficulty initiating and maintaining sleep. Frequent visits to the bathroom in the middle of the night is considered as one of the major reasons for sleep disorder. This leads to serious diseases such as depression and diabetes. In this paper, we propose to use a network of cheap low-resolution visual sensors 30 $$\times $$× 30 pixels for long-term activity analysis of a senior citizen in a service flat. The main focus of our research is on elderly behaviour analysis to detect health deterioration. Specifically, this paper treats the analysis of sleep patterns. Firstly, motion patterns are detected. Then, a rule-based approach on the motion patterns is proposed to determine the wake up time and sleep time. The nightly bathroom visit is identified using a classification-based model. In our evaluation, we performed experiments on 10i¾?months of real-life data. The ground truth is collected from the diaries in which the senior citizen wrote down his sleep time and wake up time. The results show accurate extraction of the sleep durations with an overall Mean Absolute Error MAE of 22.91i¾?min and Spearman correlation coefficient of 0.69. Finally, the nightly bathroom visits analysis indicate sleep disorder in several nights.

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

A low-resolution visual sensor network that enables monitoring of elderly people's health and safety at home, postponing institutionalized healthcare.

The demand for healthcare services for an increasing population of older adults is faced with the shortage of skilled caregivers and a constant increase in healthcare costs. In addition, the strong preference of the elderly to live independently has been driving much research on "ambient-assisted living" (AAL) systems to support aging-in-place. In this paper, we propose to employ a low-resolution image sensor network for behavior analysis of a home occupant. A network of 10 low-resolution cameras (30x30 pixels) is installed in a service flat of an elderly, based on which the user's mobility tracks are extracted using a maximum likelihood tracker. We propose a novel measure to find similar patterns of behavior between each pair of days from the user's detected positions, based on heatmaps and Earth mover's distance (EMD). Then, we use an exemplar-based approach to identify sleeping, eating, and sitting activities, and walking patterns of the elderly user for two weeks of real-life recordings. The proposed system achieves an overall accuracy of about 94%.

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.