1,563 research outputs found
Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect
Microsoft Kinect camera and its skeletal tracking capabilities have been
embraced by many researchers and commercial developers in various applications
of real-time human movement analysis. In this paper, we evaluate the accuracy
of the human kinematic motion data in the first and second generation of the
Kinect system, and compare the results with an optical motion capture system.
We collected motion data in 12 exercises for 10 different subjects and from
three different viewpoints. We report on the accuracy of the joint localization
and bone length estimation of Kinect skeletons in comparison to the motion
capture. We also analyze the distribution of the joint localization offsets by
fitting a mixture of Gaussian and uniform distribution models to determine the
outliers in the Kinect motion data. Our analysis shows that overall Kinect 2
has more robust and more accurate tracking of human pose as compared to Kinect
1.Comment: 10 pages, IEEE International Conference on Healthcare Informatics
2015 (ICHI 2015
Deep Learning-Based Human Pose Estimation: A Survey
Human pose estimation aims to locate the human body parts and build human
body representation (e.g., body skeleton) from input data such as images and
videos. It has drawn increasing attention during the past decade and has been
utilized in a wide range of applications including human-computer interaction,
motion analysis, augmented reality, and virtual reality. Although the recently
developed deep learning-based solutions have achieved high performance in human
pose estimation, there still remain challenges due to insufficient training
data, depth ambiguities, and occlusion. The goal of this survey paper is to
provide a comprehensive review of recent deep learning-based solutions for both
2D and 3D pose estimation via a systematic analysis and comparison of these
solutions based on their input data and inference procedures. More than 240
research papers since 2014 are covered in this survey. Furthermore, 2D and 3D
human pose estimation datasets and evaluation metrics are included.
Quantitative performance comparisons of the reviewed methods on popular
datasets are summarized and discussed. Finally, the challenges involved,
applications, and future research directions are concluded. We also provide a
regularly updated project page: \url{https://github.com/zczcwh/DL-HPE
Collection and analyses of crowd travel behaviour data by using smartphones
In 2010 the MOVE project started in the collection and analysis of crowd behaviour data. The two main goals of the project are first, the collection of data through the use of mobile phones. The second goal is to develop new technologies to process and mine the collected data for crowd behaviour analysis. The technology will allow to make advanced interpretations of historic and dynamic mobile crowd data coming from GSM/GPS and from different classes of users (vehicle, pedestrian, indoor/outdoor). Fusion will be made between data coming from different sources (smartphone, navigation device) and external map data. The interpretation will allow the mining of advanced features/geometry from the crowd data as well as the dynamic (travel) behavior of the population
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