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
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.
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