Occlusion-robust model learning for human pose estimation | IEEE Conference Publication | IEEE Xplore

Occlusion-robust model learning for human pose estimation

Publisher: IEEE

Abstract:

In this paper we examine the efficacy of self-occlusion-aware appearance learning for the part based model. Appearance modeling with less accurate appearance data is prob...View more

Abstract:

In this paper we examine the efficacy of self-occlusion-aware appearance learning for the part based model. Appearance modeling with less accurate appearance data is problematic because it adversely affects entire learning process. We evaluate the effectiveness of mitigating the influence of self-occluded body parts to be modeled for better appearance modeling process. To meet this end, We introduce an effective method for scoring degree of self-occlusion and we employ an approach learning a sample proportionally weighted to the score. We present our approach improves the performance of human pose estimation.
Date of Conference: 03-06 November 2015
Date Added to IEEE Xplore: 09 June 2016
ISBN Information:
Electronic ISSN: 2327-0985
Publisher: IEEE
Conference Location: Kuala Lumpur, Malaysia

References

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