Compressive evaluation in human motion tracking

  • Authors:

  • Yifan Lu;Lei Wang;Richard Hartley;Hongdong Li;Dan Xu

  • Affiliations:

  • School of Engineering, CECS, Australian National University;School of Engineering, CECS, Australian National University;School of Engineering, CECS, Australian National University and Canberra Research Labs, National ICT Australia;School of Engineering, CECS, Australian National University and Canberra Research Labs, National ICT Australia;Department of Computer Science and Engineering, SISE, Yunan University

  • Venue:
  • ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
  • Year:
  • 2010

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Abstract

The powerful theory of compressive sensing enables an efficient way to recover sparse or compressible signals from non-adaptive, sub-Nyquist-rate linear measurements. In particular, it has been shown that random projections can well approximate an isometry, provided that the number of linear measurements is no less than twice of the sparsity level of the signal. Inspired by these, we propose a compressive anneal particle filter to exploit sparsity existing in image-based human motion tracking. Instead of performing full signal recovery, we evaluate the observation likelihood directly in the compressive domain of the observed images. Moreover, we introduce a progressive multilevel wavelet decomposition staged at each anneal layer to accelerate the compressive evaluation in a coarse-to-fine fashion. The experiments with the benchmark dataset HumanEvaII show that the tracking process can be significantly accelerated, and the tracking accuracy is well maintained and comparable to the method using original image observations.