Human Pose Estimation using Deep Consensus Voting

Abstract

In this paper we consider the problem of human pose estimation from a single still image. We propose a novel approach where each location in the image votes for the position of each keypoint using a convolutional neural net. The voting scheme allows us to utilize information from the whole image, rather than rely on a sparse set of keypoint locations. Using dense, multi-target votes, not only produces good keypoint predictions, but also enables us to compute image-dependent joint keypoint probabilities by looking at consensus voting. This differs from most previous methods where joint probabilities are learned from relative keypoint locations and are independent of the image. We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration. We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets.

References in corpus (3)

• Caffe: Convolutional Architecture for Fast Feature Embedding
• Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation
• DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation

Cited by in corpus (16)

• VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
• Human pose estimation via Convolutional Part Heatmap Regression
• Learning Feature Pyramids for Human Pose Estimation
• Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation
• TFPose: Direct Human Pose Estimation with Transformers
• Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation
• Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision
• Integral Human Pose Regression
• Quantized Densely Connected U-Nets for Efficient Landmark Localization
• Learning to Refine Human Pose Estimation
• CU-Net: Coupled U-Nets
• Real-time Human Pose Estimation from Video with Convolutional Neural Networks
• DeepSkeleton: Skeleton Map for 3D Human Pose Regression
• Train Your Data Processor: Distribution-Aware and Error-Compensation Coordinate Decoding for Human Pose Estimation
• Computer Vision and Abnormal Patient Gait Assessment a Comparison of Machine Learning Models
• Simple Multi-Resolution Representation Learning for Human Pose Estimation