Exploiting Offset-guided Network for Pose Estimation and Tracking

Understand

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.