Skeleton-free body pose estimation from depth images for movement analysis

Ben Crabbe, Adeline Paiement, Sion Hannuna, Majid Mirmehdi

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)


In movement analysis frameworks, body pose may often be adequately represented in a simple, low-dimensional, and high-level space, while full body joints locations constitute excessively redundant and complex information. We propose a method for estimating body pose in such high-level pose spaces directly from a depth image without relying on intermediate skeleton-based steps. Our method is based on a convolutional neural network (CNN) that maps the depth-silhouette of a person to its position in the pose space. We apply our method to a pose representation proposed in [16] that was initially built from skeleton data. We find our estimation of pose to be consistent with the original one and suitable for use in the movement quality assessment framework of [16]. This opens the possibility of a wider application of the movement analysis method to movement types and view-angles that are not supported by the skeleton tracking algorithm.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Computer Vision Workshop (ICCVW)
Number of pages8
Publication statusPublished - 15 Dec 2015
EventChaLearn LaP workshop at ICCV 2015 - Santiago, Chile
Duration: 12 Dec 2015 → …


WorkshopChaLearn LaP workshop at ICCV 2015
Period12/12/15 → …

Structured keywords

  • Digital Health


  • Digital Health


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