Posture reconstruction using kinect with a probabilistic model

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

19 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST
PublisherAssociation for Computing Machinery
Pages117-125
ISBN (Print)9781450332538
Publication statusPublished - 11 Nov 2014

Conference

Title20th ACM Symposium on Virtual Reality Software and Technology, VRST 2014
PlaceUnited Kingdom
CityEdinburgh
Period11 - 13 November 2014

Abstract

Recent work has shown that depth image based 3D posture estimation hardware such as Kinect has made interactive applications more popular. However, it is still challenging to accurately recognize postures from a single depth camera due to the inherently noisy data derived from depth images and self-occluding action performed by the user. While previous research has shown that data-driven methods can be used to reconstruct the correct postures, they usually require a large posture database, which greatly limit the usability for systems with constrained hardware such as game console. To solve this problem, we present a new probabilistic framework to enhance the accuracy of the postures live captured by Kinect. We adopt the Gaussian Process model as a prior to leverage position data obtained from Kinect and markerbased motion capture system. We also incorporate a temporal consistency term into the optimization framework to constrain the velocity variations between successive frames. To ensure that the reconstructed posture resembles the observed input data from Kinect when its tracking result is good, we embed joint reliability into the optimization framework. Experimental results demonstrate that our system can generate high quality postures even under severe self-occlusion situations, which is beneficial for real-time posture based applications such as motion-based gaming and sport training.

Research Area(s)

  • Gaussian process, Kinect, Posture reconstruction

Citation Format(s)

Posture reconstruction using kinect with a probabilistic model. / Zhou, Liuyang; Liu, Zhiguang; Leung, Howard; Shum, Hubert P. H.

Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST. Association for Computing Machinery, 2014. p. 117-125.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review