基于CNN的检测系统的灵活俯视图人体姿态估计

Ryuji Go, Y. Aoki
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引用次数: 6

摘要

提出了一种基于deeppose的姿态估计系统,该系统可以灵活地改变顶视图图像的边界框范围。我们的目的是将人检测系统和姿态估计系统连接起来。我们介绍了边界盒课程学习(BCL)和循环姿态估计(RPE)。BCL是受课程学习启发的CNN学习技术。RPE是一种姿态估计的循环过程,根据估计结果确定边界框范围。我们在原始的俯视图数据集上展示了所提出的方法与常规学习的基于cnn的姿态估计器的效果。
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Flexible top-view human pose estimation for detection system via CNN
We propose the DeepPose-based pose estimation system that is flexible with the change of bounding-box range for top-view images. Our purpose is to link person detection system and pose estimation system. We introduce Bounding-box Curriculum Learning (BCL) and Recurrent Pose Estimation (RPE). BCL is a learning technique of CNN inspired from Curriculum Learning. RPE is a recurrent process of pose estimation that fixes the bounding-box range in response to the estimated results. We show the effect of proposed methods compared to normal learned CNN-based pose estimator on our original top-view dataset.
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