2,304 research outputs found
RGBD Datasets: Past, Present and Future
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style
RGB-D datasets using microsoft kinect or similar sensors: a survey
RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms
Intensive-care unit patients monitoring by computer vision system
Treballs Finals de Grau d'Enginyeria Informà tica, Facultat de Matemà tiques, Universitat de Barcelona, Any: 2013, Director: Santi Seguà MesquidaIn this project, we propose an automatic computer vision system for patient monitoring at the
Intensive-Care Unit (ICU). These patients require constant monitoring and, due to the high costs
associated to equipment and staff necessary, the design of an automatic system would be helpful.
Depth imaging technology has advanced dramatically over the last few years, finally reaching a consumer price point with the launch of Kinect. These depth images are not affected by the lighting conditions and provide us a good vision, even without any light, so we can monitorize the patients 24 hours a day.
In this project, we worked on two of the parts of the object detection systems: the descriptor and
classifier.
Concerning the descriptor, we analyzed the performance of one of the most used descriptors for object detection in RGB images, the Histogram of Oriented Gradients, and we have proposed a
descriptor designed for depth images. It is shown that the combination of these two descriptors
increases system accuracy.
As to the detection, we have done various tests. We analyzed the detection of patient body parts
separately, and we have used a model where the patient is divided into multiple parts and each part is modeled with a set of templates, demonstrating that the use of a model helps to improve detection
HeadOn: Real-time Reenactment of Human Portrait Videos
We propose HeadOn, the first real-time source-to-target reenactment approach
for complete human portrait videos that enables transfer of torso and head
motion, face expression, and eye gaze. Given a short RGB-D video of the target
actor, we automatically construct a personalized geometry proxy that embeds a
parametric head, eye, and kinematic torso model. A novel real-time reenactment
algorithm employs this proxy to photo-realistically map the captured motion
from the source actor to the target actor. On top of the coarse geometric
proxy, we propose a video-based rendering technique that composites the
modified target portrait video via view- and pose-dependent texturing, and
creates photo-realistic imagery of the target actor under novel torso and head
poses, facial expressions, and gaze directions. To this end, we propose a
robust tracking of the face and torso of the source actor. We extensively
evaluate our approach and show significant improvements in enabling much
greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at
Siggraph'1
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