A low resolution multi-camera system for person tracking

Philips

et al. 2016

J. Electron. Imaging

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Recent advancements in visual sensor technologies have made behavior analysis practical for in-home monitoring systems. The current in-home monitoring systems face several challenges: (1) visual sensor calibration is a difficult task and not practical in real-life because of the need for recalibration when the visual sensors are moved accidentally by a caregiver or the senior citizen, (2) privacy concerns, and (3) the high hardware installation cost. We propose to use a network of cheap low-resolution visual sensors (30×30 pixels) for long-term behavior analysis. The behavior analysis starts by visual feature selection based on foreground/background detection to track the motion level in each visual sensor. Then a hidden Markov model (HMM) is used to estimate the user’s locations without calibration. Finally, an activity discovery approach is proposed using spatial and temporal contexts. We performed experiments on 10 months of real-life data. We show that the HMM approach outperforms the k-nearest neighbor classifier against ground truth for 30 days. Our framework is able to discover 13 activities of daily livings (ADL parameters). More specifically, we analyze mobility patterns and some of the key ADL parameters to detect increasing or decreasing health conditions

Section: Related Workmentioning

confidence: 99%

Behavior analysis for elderly care using a network of low-resolution visual sensors

Philips

et al. 2016

J. Electron. Imaging

Self Cite

“…In an earlier work [10], we used low-resolution cameras of [7] for person tracking, and reported promising tracking performance. We use the same low-resolution cameras in the current study.…”

Section: Service Flat Setupmentioning

confidence: 97%

“…We follow the same approach as in [10] based on correlation method [4] and maximum likelihood tracker [5], due to their good performance with low-resolution cameras of [7]. The overall system architecture can be seen in Figure 3.…”

Section: Service Flat Setupmentioning

confidence: 99%

Behavior Analysis for Aging-in-Place using Similarity Heatmaps

Proceedings of the International Conference on Distributed Smart Cameras

et al. 2014

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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 (30×30 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%.

“…As a result, visitor detection methods from literature are also only analyzed over short periods. Visual sensors have shown promising results in person tracking [11,17] and behavior analysis [25,12]. They are designed to offer an acceptable sensing mechanism for addressing the privacy concerns of the users, while the kinect sensor requires post-processing algorithms to solve the privacy concern problem [6].…”

Section: Related Workmentioning

confidence: 99%

Detection of visitors in elderly care using a low-resolution visual sensor network

Proceedings of the 9th International Conference on Distributed Smart Cameras

Haerenborgh

et al. 2015

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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.