Real-time pose estimation of articulated objects using low-level motion

Ben Daubney*, David Gibson, Neill Campbell

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a method that is capable of tracking and estimating pose of articulated objects in real-time. This is achieved by using a bottom-up approach to detect instances of the object in each frame, these detections are then linked together using a high-level a priori motion model. Unlike other approaches that rely on appearance, our method is entirely dependent on motion; initial low-level part detection is based on how a region moves as opposed to its appearance. This work is best described as Pictorial Structures using motion. A sparse cloud of points extracted using a standard feature tracker are used as observational data, this data contains noise that is not Gaussian in nature but systematic due to tracking errors. Using a probabilistic framework we are able to overcome both corrupt and missing data whilst still inferring new poses from a generative model. Our approach requires no manual initialisation and we show results for a number of complex scenes and different classes of articulated object, this demonstrates both the robustness and versatility of the presented technique.

Original languageEnglish
Title of host publication2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008
Subtitle of host publicationCVPR 2008
PublisherInstitute of Electrical and Electronics Engineers
Pages1460-1467
Number of pages8
ISBN (Print)978-1-4244-2242-5
Publication statusPublished - 2008
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition - Anchorage
Duration: 23 Jun 200828 Jun 2008

Publication series

NamePROCEEDINGS - IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION
PublisherIEEE
ISSN (Print)1063-6919

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition
CityAnchorage
Period23/06/0828/06/08

Keywords

  • Perception
  • motion estimation
  • motion detection
  • object detection
  • data mining
  • tracking
  • dynamic programming
  • belief propagation
  • pose estimation
  • image motion analysis

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