论文标题
无监督的姿势估算的端到端框架
An End-to-End Framework for Unsupervised Pose Estimation of Occluded Pedestrians
论文作者
论文摘要
野外的姿势估计是一个具有挑战性的问题，尤其是在（i）不同程度和（ii）拥挤的室外场景的情况下。在类似情况下，大多数现有的姿势估计研究都没有报告表现。此外，在任何相关的标准数据集中尚未提供针对人物的封闭部分的姿势注释，这反过来又为所需的研究带来了进一步的困难，以构成对遮挡人类的整个数字的姿势估计。众所周知的行人检测数据集（例如Citypersons）包含室外场景的样本，但不包括姿势注释。在这里，我们提出了一个新型的多任务框架，用于端到端训练，以对行人的整个姿势估算，包括任何形式的遮挡情况。为了解决培训网络的问题，我们利用姿势估计数据集，MS-Coco，并采用无监督的对抗实例级级域的适应性，以估算整个遮挡的行人的姿势。实验研究表明，在两个基准数据集中，提出的框架的表现优于SOTA的姿势估计，实例分割和行人检测的结果，实例分割和行人检测结果。
Pose estimation in the wild is a challenging problem, particularly in situations of (i) occlusions of varying degrees and (ii) crowded outdoor scenes. Most of the existing studies of pose estimation did not report the performance in similar situations. Moreover, pose annotations for occluded parts of human figures have not been provided in any of the relevant standard datasets which in turn creates further difficulties to the required studies for pose estimation of the entire figure of occluded humans. Well known pedestrian detection datasets such as CityPersons contains samples of outdoor scenes but it does not include pose annotations. Here, we propose a novel multi-task framework for end-to-end training towards the entire pose estimation of pedestrians including in situations of any kind of occlusion. To tackle this problem for training the network, we make use of a pose estimation dataset, MS-COCO, and employ unsupervised adversarial instance-level domain adaptation for estimating the entire pose of occluded pedestrians. The experimental studies show that the proposed framework outperforms the SOTA results for pose estimation, instance segmentation and pedestrian detection in cases of heavy occlusions (HO) and reasonable + heavy occlusions (R + HO) on the two benchmark datasets.
