5,731 research outputs found
Radar and RGB-depth sensors for fall detection: a review
This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing
Underwater reconstruction using depth sensors
In this paper we describe experiments in which we acquire range images of underwater surfaces with four types of depth sensors and attempt to reconstruct underwater surfaces. Two conditions are tested: acquiring range images by submersing the sensors and by holding the sensors over the water line and recording through water. We found out that only the Kinect sensor is able to acquire depth images of submersed surfaces by holding the sensor above water. We compare the reconstructed underwater geometry with meshes obtained when the surfaces were not submersed. These findings show that 3D underwater reconstruction using depth sensors is possible, despite the high water absorption of the near infrared spectrum in which these sensors operate
Real-time computation of distance to dynamic obstacles with multiple depth sensors
We present an efficient method to evaluate distances between dynamic obstacles and a number of points of interests (e.g., placed on the links of a robot) when using multiple depth cameras. A depth-space oriented discretization of the Cartesian space is introduced that represents at best the workspace monitored by a depth camera, including occluded points. A depth grid map can be initialized off line from the arrangement of the multiple depth cameras, and its peculiar search characteristics allows fusing on line the information given by the multiple sensors in a very simple and fast way. The real-time performance of the proposed approach is shown by means of collision avoidance experiments where two Kinect sensors monitor a human-robot coexistence task
Calibrating Depth Sensors with a Genetic Algorithm
In this report, we deal with the optimization of the transformation estimate between the coordinate systems of depth sensors, \ie sensors that produce 3D measurements. For that, we present a novel method using a genetic algorithm to refine the six degrees of freedom (6 DoF) transformation via three rotational and three translational offsets. First, we demonstrate the necessity for an accurate depth sensor calibration using a depth error model of stereo cameras. The fusion of stereo disparity assumes a Gaussian disparity error distribution, which we examine with different stereo matching algorithms on the widely-used KITTI visual odometry dataset. Our analysis shows that the existing calibration is not adequate for accurate disparity fusion. As a consequence, we employ our genetic algorithm on this particular dataset, which results in a greatly improved calibration between the mounted stereo camera and the Lidar. Thus, stereo disparity estimates show improved results in quantitative evaluations
Duodepth: Static Gesture Recognition Via Dual Depth Sensors
Static gesture recognition is an effective non-verbal communication channel
between a user and their devices; however many modern methods are sensitive to
the relative pose of the user's hands with respect to the capture device, as
parts of the gesture can become occluded. We present two methodologies for
gesture recognition via synchronized recording from two depth cameras to
alleviate this occlusion problem. One is a more classic approach using
iterative closest point registration to accurately fuse point clouds and a
single PointNet architecture for classification, and the other is a dual
Point-Net architecture for classification without registration. On a manually
collected data-set of 20,100 point clouds we show a 39.2% reduction in
misclassification for the fused point cloud method, and 53.4% for the dual
PointNet, when compared to a standard single camera pipeline.Comment: 26th International Conference on Image Processin
Depth sensors in augmented reality solutions. Literature review
The emergence of depth sensors has made it possible to track – not only monocular
cues – but also the actual depth values of the environment. This is especially
useful in augmented reality solutions, where the position and orientation (pose) of
the observer need to be accurately determined. This allows virtual objects to be
installed to the view of the user through, for example, a screen of a tablet or augmented
reality glasses (e.g. Google glass, etc.). Although the early 3D sensors have
been physically quite large, the size of these sensors is decreasing, and possibly –
eventually – a 3D sensor could be embedded – for example – to augmented reality
glasses. The wider subject area considered in this review is 3D SLAM methods,
which take advantage of the 3D information available by modern RGB-D sensors,
such as Microsoft Kinect. Thus the review for SLAM (Simultaneous Localization
and Mapping) and 3D tracking in augmented reality is a timely subject. We also try
to find out the limitations and possibilities of different tracking methods, and how
they should be improved, in order to allow efficient integration of the methods to
the augmented reality solutions of the future.Siirretty Doriast
Depth sensors in augmented reality solutions. Literature review
The emergence of depth sensors has made it possible to track – not only monocular
cues – but also the actual depth values of the environment. This is especially
useful in augmented reality solutions, where the position and orientation (pose) of
the observer need to be accurately determined. This allows virtual objects to be
installed to the view of the user through, for example, a screen of a tablet or augmented
reality glasses (e.g. Google glass, etc.). Although the early 3D sensors have
been physically quite large, the size of these sensors is decreasing, and possibly –
eventually – a 3D sensor could be embedded – for example – to augmented reality
glasses. The wider subject area considered in this review is 3D SLAM methods,
which take advantage of the 3D information available by modern RGB-D sensors,
such as Microsoft Kinect. Thus the review for SLAM (Simultaneous Localization
and Mapping) and 3D tracking in augmented reality is a timely subject. We also try
to find out the limitations and possibilities of different tracking methods, and how
they should be improved, in order to allow efficient integration of the methods to
the augmented reality solutions of the future.Siirretty Doriast
Depth Superresolution using Motion Adaptive Regularization
Spatial resolution of depth sensors is often significantly lower compared to
that of conventional optical cameras. Recent work has explored the idea of
improving the resolution of depth using higher resolution intensity as a side
information. In this paper, we demonstrate that further incorporating temporal
information in videos can significantly improve the results. In particular, we
propose a novel approach that improves depth resolution, exploiting the
space-time redundancy in the depth and intensity using motion-adaptive low-rank
regularization. Experiments confirm that the proposed approach substantially
improves the quality of the estimated high-resolution depth. Our approach can
be a first component in systems using vision techniques that rely on high
resolution depth information
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