Tracking Human Pose Using Max-Margin Markov Models

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Lin ZhaoXuelong Li

Abstract

We present a new method for tracking human pose by employing max-margin Markov models. Representing a human body by part-based models, such as pictorial structure, the problem of pose tracking can be modeled by a discrete Markov random field. Considering max-margin Markov networks provide an efficient way to deal with both structured data and strong generalization guarantees, it is thus natural to learn the model parameters using the max-margin technique. Since tracking human pose needs to couple limbs in adjacent frames, the model will introduce loops and will be intractable for learning and inference. Previous work has resorted to pose estimation methods, which discard temporal information by parsing frames individually. Alternatively, approximate inference strategies have been used, which can overfit to statistics of a particular data set. Thus, the performance and generalization of these methods are limited. In this paper, we approximate the full model by introducing an ensemble of two tree-structured sub-models, Markov networks for spatial parsing and Markov chains for temporal parsing. Both models can be trained jointly using the max-margin technique, and an iterative parsing process is proposed to achieve the ensemble in...Continue Reading

References

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Jul 17, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Pedro F FelzenszwalbDeva Ramanan
Jun 26, 2013·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Huchuan LuYi Xiao
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Jun 24, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Fahad Shahbaz KhanCarlo Gatta
Apr 1, 2015·IEEE Transactions on Neural Networks and Learning Systems·Lin ZhaoXuelong Li
Aug 16, 2015·IEEE Transactions on Neural Networks and Learning Systems·Dapeng TaoXu Lin
Nov 6, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Chang XuChao Xu

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Citations

May 31, 2016·Sensors·Ricardo Acevedo-AvilaAndres Garcia-Garcia
Jun 24, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Baochang ZhangLing Shao

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