37,516 research outputs found
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance
One in twenty-five patients admitted to a hospital will suffer from a
hospital acquired infection. If we can intelligently track healthcare staff,
patients, and visitors, we can better understand the sources of such
infections. We envision a smart hospital capable of increasing operational
efficiency and improving patient care with less spending. In this paper, we
propose a non-intrusive vision-based system for tracking people's activity in
hospitals. We evaluate our method for the problem of measuring hand hygiene
compliance. Empirically, our method outperforms existing solutions such as
proximity-based techniques and covert in-person observational studies. We
present intuitive, qualitative results that analyze human movement patterns and
conduct spatial analytics which convey our method's interpretability. This work
is a step towards a computer-vision based smart hospital and demonstrates
promising results for reducing hospital acquired infections.Comment: Machine Learning for Healthcare Conference (MLHC
Cognitive visual tracking and camera control
Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
Advantages of 3D time-of-flight range imaging cameras in machine vision applications
Machine vision using image processing of traditional intensity images is in wide spread use. In many situations environmental conditions or object colours or shades cannot be controlled, leading to difficulties in correctly processing the images and requiring complicated processing algorithms. Many of these complications can be avoided by using range image data, instead of intensity data. This is because range image data represents the physical properties of object location and shape, practically independently of object colour or shading. The advantages of range image processing are presented, along with three example applications that show how robust machine vision results can be obtained with relatively simple range image processing in real-time applications
Evaluation of optimisation techniques for multiscopic rendering
A thesis submitted to the University of Bedfordshire in fulfilment of the requirements for the degree of Master of Science by ResearchThis project evaluates different performance optimisation techniques applied to stereoscopic and multiscopic rendering for interactive applications. The artefact
features a robust plug-in package for the Unity game engine. The thesis provides background information for the performance optimisations, outlines all the findings, evaluates the optimisations and provides suggestions for future work.
Scrum development methodology is used to develop the artefact and quantitative research methodology is used to evaluate the findings by measuring performance.
This project concludes that the use of each performance optimisation has specific use case scenarios in which performance benefits. Foveated rendering provides
greatest performance increase for both stereoscopic and multiscopic rendering but is also more computationally intensive as it requires an eye tracking solution.
Dynamic resolution is very beneficial when overall frame rate smoothness is needed and frame drops are present. Depth optimisation is beneficial for vast open environments but can lead to decreased performance if used inappropriately
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
We present an approach that uses a multi-camera system to train fine-grained
detectors for keypoints that are prone to occlusion, such as the joints of a
hand. We call this procedure multiview bootstrapping: first, an initial
keypoint detector is used to produce noisy labels in multiple views of the
hand. The noisy detections are then triangulated in 3D using multiview geometry
or marked as outliers. Finally, the reprojected triangulations are used as new
labeled training data to improve the detector. We repeat this process,
generating more labeled data in each iteration. We derive a result analytically
relating the minimum number of views to achieve target true and false positive
rates for a given detector. The method is used to train a hand keypoint
detector for single images. The resulting keypoint detector runs in realtime on
RGB images and has accuracy comparable to methods that use depth sensors. The
single view detector, triangulated over multiple views, enables 3D markerless
hand motion capture with complex object interactions.Comment: CVPR 201
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
We present the first real-time method to capture the full global 3D skeletal
pose of a human in a stable, temporally consistent manner using a single RGB
camera. Our method combines a new convolutional neural network (CNN) based pose
regressor with kinematic skeleton fitting. Our novel fully-convolutional pose
formulation regresses 2D and 3D joint positions jointly in real time and does
not require tightly cropped input frames. A real-time kinematic skeleton
fitting method uses the CNN output to yield temporally stable 3D global pose
reconstructions on the basis of a coherent kinematic skeleton. This makes our
approach the first monocular RGB method usable in real-time applications such
as 3D character control---thus far, the only monocular methods for such
applications employed specialized RGB-D cameras. Our method's accuracy is
quantitatively on par with the best offline 3D monocular RGB pose estimation
methods. Our results are qualitatively comparable to, and sometimes better
than, results from monocular RGB-D approaches, such as the Kinect. However, we
show that our approach is more broadly applicable than RGB-D solutions, i.e. it
works for outdoor scenes, community videos, and low quality commodity RGB
cameras.Comment: Accepted to SIGGRAPH 201
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