Human Pose Estimation Using Deep Consensus Voting

Conference paper

DOI: 10.1007/978-3-319-46475-6_16

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9906)
Cite this paper as:
Lifshitz I., Fetaya E., Ullman S. (2016) Human Pose Estimation Using Deep Consensus Voting. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9906. Springer, Cham


In this paper we consider the problem of human pose estimation from a single still image. We propose a novel approach where each location in the image votes for the position of each keypoint using a convolutional neural net. The voting scheme allows us to utilize information from the whole image, rather than rely on a sparse set of keypoint locations. Using dense, multi-target votes, not only produces good keypoint predictions, but also enables us to compute image-dependent joint keypoint probabilities by looking at consensus voting. This differs from most previous methods where joint probabilities are learned from relative keypoint locations and are independent of the image. We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration. We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets.

Supplementary material

419974_1_En_16_MOESM1_ESM.pdf (2.9 mb)
Supplementary material 1 (pdf 2933 KB)

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Weizmann Institute of ScienceRehovotIsrael

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