4,277 research outputs found
Markerless View Independent Gait Analysis with Self-camera Calibration
We present a new method for viewpoint independent markerless gait analysis. The system uses a single camera, does not require camera calibration and works with a wide range of directions of walking. These properties make the proposed method particularly suitable for identification by gait, where the advantages of completely unobtrusiveness, remoteness and covertness of the biometric system preclude the availability of camera information and use of marker based technology. Tests on more than 200 video sequences with subjects walking freely along different walking directions have been performed. The obtained results show that markerless gait analysis can be achieved without any knowledge of internal or external camera parameters and that the obtained data that can be used for gait biometrics purposes. The performance of the proposed method is particularly encouraging for its appliance in surveillance scenarios
Markerless Human Motion Analysis
Measuring and understanding human motion is crucial in several domains,
ranging from neuroscience, to rehabilitation and sports biomechanics. Quantitative
information about human motion is fundamental to study how our
Central Nervous System controls and organizes movements to functionally
evaluate motor performance and deficits. In the last decades, the research in
this field has made considerable progress. State-of-the-art technologies that
provide useful and accurate quantitative measures rely on marker-based systems.
Unfortunately, markers are intrusive and their number and location must
be determined a priori. Also, marker-based systems require expensive laboratory
settings with several infrared cameras. This could modify the naturalness
of a subject\u2019s movements and induce discomfort. Last, but not less important,
they are computationally expensive in time and space. Recent advances on
markerless pose estimation based on computer vision and deep neural networks
are opening the possibility of adopting efficient video-based methods
for extracting movement information from RGB video data. In this contest,
this thesis presents original contributions to the following objectives: (i) the
implementation of a video-based markerless pipeline to quantitatively characterize
human motion; (ii) the assessment of its accuracy if compared with
a gold standard marker-based system; (iii) the application of the pipeline to
different domains in order to verify its versatility, with a special focus on the
characterization of the motion of preterm infants and on gait analysis. With
the proposed approach we highlight that, starting only from RGB videos and
leveraging computer vision and machine learning techniques, it is possible to
extract reliable information characterizing human motion comparable to that
obtained with gold standard marker-based systems
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
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