Structured Feature Learning for Pose Estimation
arXiv:1603.09065
Abstract
In this paper, we propose a structured feature learning framework to reason the correlations among body joints at the feature level in human pose estimation. Different from existing approaches of modelling structures on score maps or predicted labels, feature maps preserve substantially richer descriptions of body joints. The relationships between feature maps of joints are captured with the introduced geometrical transform kernels, which can be easily implemented with a convolution layer. Features and their relationships are jointly learned in an end-to-end learning system. A bi-directional tree structured model is proposed, so that the feature channels at a body joint can well receive information from other joints. The proposed framework improves feature learning substantially. With very simple post processing, it reaches the best mean PCP on the LSP and FLIC datasets. Compared with the baseline of learning features at each joint separately with ConvNet, the mean PCP has been improved by 18% on FLIC. The code is released to the public.
Accepted by CVPR2016
References in corpus (10)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- DeepPose: Human Pose Estimation via Deep Neural Networks
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- Conditional Random Fields as Recurrent Neural Networks
- Going Deeper with Convolutions
- Visualizing and Understanding Convolutional Networks
- Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation
- DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
- Deformable Part Models are Convolutional Neural Networks
- An Expressive Deep Model for Human Action Parsing from A Single Image
Cited by in corpus (4)
- Joint Multi-Person Pose Estimation and Semantic Part Segmentation
- Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
- ViP-CNN: Visual Phrase Guided Convolutional Neural Network
- 3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information