19,207 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
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions
3D action recognition has broad applications in human-computer interaction
and intelligent surveillance. However, recognizing similar actions remains
challenging since previous literature fails to capture motion and shape cues
effectively from noisy depth data. In this paper, we propose a novel two-layer
Bag-of-Visual-Words (BoVW) model, which suppresses the noise disturbances and
jointly encodes both motion and shape cues. First, background clutter is
removed by a background modeling method that is designed for depth data. Then,
motion and shape cues are jointly used to generate robust and distinctive
spatial-temporal interest points (STIPs): motion-based STIPs and shape-based
STIPs. In the first layer of our model, a multi-scale 3D local steering kernel
(M3DLSK) descriptor is proposed to describe local appearances of cuboids around
motion-based STIPs. In the second layer, a spatial-temporal vector (STV)
descriptor is proposed to describe the spatial-temporal distributions of
shape-based STIPs. Using the Bag-of-Visual-Words (BoVW) model, motion and shape
cues are combined to form a fused action representation. Our model performs
favorably compared with common STIP detection and description methods. Thorough
experiments verify that our model is effective in distinguishing similar
actions and robust to background clutter, partial occlusions and pepper noise
Fall detection and activity recognition using human skeleton features
Human activity recognition has attracted the attention of researchers around the world. This is an interesting problem that can be addressed in different ways. Many approaches have been presented during the last years. These applications present solutions to recognize different kinds of activities such as if the person is walking, running, jumping, jogging, or falling, among others. Amongst all these activities, fall detection has special importance because it is a common dangerous event for people of all ages with a more negative impact on the elderly population. Usually, these applications use sensors to detect sudden changes in the movement of the person. These kinds of sensors can be embedded in smartphones, necklaces, or smart wristbands to make them “wearable” devices. The main inconvenience is that these devices have to be placed on the subjects’ bodies. This might be uncomfortable and is not always feasible because this type of sensor must be monitored constantly, and can not be used in open spaces with unknown people. In this way, fall detection from video camera images presents some advantages over the wearable sensor-based approaches. This paper presents a vision-based approach to fall detection and activity recognition. The main contribution of the proposed method is to detect falls only by using images from a standard video-camera without the need to use environmental sensors. It carries out the detection using human skeleton estimation for features extraction. The use of human skeleton detection opens the possibility for detecting not only falls but also different kind of activities for several subjects in the same scene. So this approach can be used in real environments, where a large number of people may be present at the same time. The method is evaluated with the UP-FALL public dataset and surpasses the performance of other fall detection and activities recognition systems that use that dataset
A multi-scale filament extraction method: getfilaments
Far-infrared imaging surveys of Galactic star-forming regions with Herschel
have shown that a substantial part of the cold interstellar medium appears as a
fascinating web of omnipresent filamentary structures. This highly anisotropic
ingredient of the interstellar material further complicates the difficult
problem of the systematic detection and measurement of dense cores in the
strongly variable but (relatively) isotropic backgrounds. Observational
evidence that stars form in dense filaments creates severe problems for
automated source extraction methods that must reliably distinguish sources not
only from fluctuating backgrounds and noise, but also from the filamentary
structures. A previous paper presented the multi-scale, multi-wavelength source
extraction method getsources based on a fine spatial scale decomposition and
filtering of irrelevant scales from images. In this paper, a multi-scale,
multi-wavelength filament extraction method getfilaments is presented that
solves this problem, substantially improving the robustness of source
extraction with getsources in filamentary backgrounds. The main difference is
that the filaments extracted by getfilaments are now subtracted by getsources
from detection images during source extraction, greatly reducing the chances of
contaminating catalogs with spurious sources. The intimate physical
relationship between forming stars and filaments seen in Herschel observations
demands that accurate filament extraction methods must remove the contribution
of sources and that accurate source extraction methods must be able to remove
underlying filamentary structures. Source extraction with getsources now
provides researchers also with clean images of filaments, free of sources,
noise, and isotropic backgrounds.Comment: 15 pages, 19 figures, to be published in Astronomy & Astrophysics;
language polished for better readabilit
- …