Abstract—Human pose estimation is an active research topic since for decades, and it has immediate applications in various tasks such as action understanding. Although accurate pose estimation is an important requirement, joint occlusion and various gestures of a person often result in deviated pose predictions. In this paper, we aim to correct such outliers included in pose estimation results. We propose a method to generate training data which is effective for learning models for outlier correction.
Index Terms—human pose estimation, machine learning, outlier correction
Cite: Kazumasa Oniki, Toshiki Kikuchi, and Yuko Ozasa, "Training Data Generation Based on Observation Probability Density for Human Pose Refinement," Journal of Image and Graphics, Vol. 9, No. 2, pp. 50-54, June 2021. doi: 10.18178/joig.9.2.50-54
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