Exploiting Offset-guided Network for Pose Estimation and Tracking

Abstract

Human pose estimation has witnessed a significant advance thanks to the development of deep learning. Recent human pose estimation approaches tend to directly predict the location heatmaps, which causes quantization errors and inevitably deteriorates the performance within the reduced network output. Aim at solving it, we revisit the heatmap-offset aggregation method and propose the Offset-guided Network (OGN) with an intuitive but effective fusion strategy for both two-stages pose estimation and Mask R-CNN. For two-stages pose estimation, a greedy box generation strategy is also proposed to keep more necessary candidates while performing person detection. For mask R-CNN, ratio-consistent is adopted to improve the generalization ability of the network. State-of-the-art results on COCO and PoseTrack dataset verify the effectiveness of our offset-guided pose estimation and tracking.

To appear in CVPR-2019 VUHCS Workshop

References in corpus (5)

• DeepPose: Human Pose Estimation via Deep Neural Networks
• Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
• Joint Flow: Temporal Flow Fields for Multi Person Tracking
• Multi-hierarchical Independent Correlation Filters for Visual Tracking
• End-to-end Video-level Representation Learning for Action Recognition

Cited by in corpus (7)

• Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models
• Self-supervised Keypoint Correspondences for Multi-Person Pose Estimation and Tracking in Videos
• The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation
• FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks
• Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation
• Human Following for Wheeled Robot with Monocular Pan-tilt Camera
• High Performance Visual Object Tracking with Unified Convolutional Networks