Deep learning for integrated hand detection and pose estimation
We propose a novel framework which integrates human hand detection and pose estimation into one single pipeline. Unlike most of previous works which only focus on the pose estimation part subject to some strong assumptions or relying on a weak detector to detect human hands, we employ a deep learning architecture to complete both aforementioned tasks. By letting three different neural networks share the convolutional layers, this deeply learning architecture can efficiently and accurately detect human hands and compute their hand pose configuration. Moreover, we propose a new energy function to optimize the predicted result of convolutional neural network. To validate the proposed framework, experiments have been conducted and the results show that our approach is highly reliable and suitable for real world applications.
2016 23rd International Conference on Pattern Recognition (ICPR)