Deep Convolutional Neural Networks for Efficient Pose Estimation in Gesture Videos

@inproceedings{Pfister2014DeepCN,
  title={Deep Convolutional Neural Networks for Efficient Pose Estimation in Gesture Videos},
  author={Tomas Pfister and Karen Simonyan and James Charles and Andrew Zisserman},
  booktitle={ACCV},
  year={2014}
}
Our objective is to efficiently and accurately estimate the upper body pose of humans in gesture videos. To this end, we build on the recent successful applications of deep convolutional neural networks (ConvNets). Our novelties are: (i) our method is the first to our knowledge to use ConvNets for estimating human pose in videos; (ii) a new network that exploits temporal information from multiple frames, leading to better performance; (iii) showing that pre-segmenting the foreground of the… CONTINUE READING
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