# Exploiting Offset-guided Network for Pose Estimation and Tracking Source: [Exploiting Offset-guided Network for Pose Estimation and Tracking]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:36:37.771Z Authors: Rui Zhang, Zheng Zhu, Peng Li, Rui Wu, Chaoxu Guo, Guan Huang, Hailun Xia Published: 2019-06-04T11:05:24.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()