Ensemble convolutional neural networks for pose estimation

Yuki Kawana, Norimichi Ukita, Jia Bin Huang, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Human pose estimation is a challenging task due to significant appearance variations. An ensemble of models, each of which is optimized for a limited variety of poses, is capable of modeling a large variety of human body configurations.However, ensembling models is not a straightforward task due to the complex interdependence among noisy and ambiguous pose estimation predictions acquired by each model.We propose to capture this complex interdependence using a convolutional neural network. Our network achieves this interdependence representation using a combination of deep convolution and deconvolution layers for robust and accurate pose estimation. We evaluate the proposed ensemble model on publicly available datasets and show that our model compares favorably against baseline models and state-of-the-art methods.

Original languageEnglish
Pages (from-to)62-74
Number of pages13
JournalComputer Vision and Image Understanding
Publication statusPublished - 2018 Apr

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Inc.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


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