Behavior analysis for aging-in-place using similarity heatmaps

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%

Distributed multi-class road user tracking in multi-camera network for smart traffic applications

Reliable tracking of road users is one of the important tasks in smart traffic applications. In these applications, a network of cameras is often used to extend the coverage. However, efficient usage of information from cameras which observe the same road user from different view points is seldom explored. In this paper, we present a distributed multi-camera tracker which efficiently uses information from all cameras with overlapping views to accurately track various classes of road users. Our method is designed for deployment on smart camera networks so that most computer vision tasks are executed locally on smart cameras and only concise high-level information is sent to a fusion node for global joint tracking. We evaluate the performance of our tracker on a challenging real-world traffic dataset in an aspect of Turn Movement Count (TMC) application and achieves high accuracy of 93%and 83% on vehicles and cyclist respectively. Moreover, performance testing in anomaly detection shows that the proposed method provides reliable detection of abnormal vehicle and pedestrian trajectories

PhD Forum: illumination-robust foreground detection for multi-camera occupancy mapping

Foreground detection is an essential preprocessing step for many image processing applications such as object tracking, human action recognition, pose estimation and occupancy mapping. Many existing techniques only perform well under steady illumination. Some approaches have been introduced to detect foreground under varying or sudden changes in illumination but the problem remains challenging. In this paper, we introduce a new texture-based foreground detection method which is robust to illumination change. Our method detects foreground by finding the correlation between the current frame and a background model. A region with low correlation is detected as foreground. We compare the performance of our proposed technique with other techniques from literature (edge-based, ViBe and Gaussian mixture model) as a preprocessing step of the multi-camera occupancy mapping system. The evaluation demonstrates that our technique outperforms the other methods in term of object loss

Human mobility monitoring in very low resolution visual sensor network

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

A house that knows where you are and understands your behavior

In near future, a house that you live in will know where you are and automatically adjust its configurations such as switching on the light of the room when you enter the room. Your walking paths over long period time can be recorded and then analyzed statically to recognized the behavior pattern. The automatic recognition of behavior pattern is very useful in elderly care to detect the behavior changes in elderly people living all by themselves. For the task of localizing persons in the room, one would use radio based tracking technologies. However, these technologies require attaching electronics device to a person to be tracked. To tackle this limitation, a visual tracking technologies can be used as low cost digital cameras are widely available nowadays. The limitation of the low cost digital cameras in this application context is that view from a single camera is often not enough to cover the whole room. Thus a network of cameras is used to sufficiently cover every room in the house. Each camera estimates a person’s 3D locally using computer vision techniques. These estimated positions are sent to central controller to make more reliable and accurate joint estimates