تقرير
Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation
| العنوان: | Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation |
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| المؤلفون: | Banik, Soubarna, Gschoßmann, Patricia, Garcia, Alejandro Mendoza, Knoll, Alois |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Occlusion is an omnipresent challenge in 3D human pose estimation (HPE). In spite of the large amount of research dedicated to 3D HPE, only a limited number of studies address the problem of occlusion explicitly. To fill this gap, we propose to combine exploitation of spatio-temporal features with synthetic occlusion augmentation during training to deal with occlusion. To this end, we build a spatio-temporal 3D HPE model, StridedPoseGraphFormer based on graph convolution and transformers, and train it using occlusion augmentation. Unlike the existing occlusion-aware methods, that are only tested for limited occlusion, we extensively evaluate our method for varying degrees of occlusion. We show that our proposed method compares favorably with the state-of-the-art (SoA). Our experimental results also reveal that in the absence of any occlusion handling mechanism, the performance of SoA 3D HPE methods degrades significantly when they encounter occlusion. Comment: IEEE IJCNN 2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2304.12069 |
| رقم الأكسشن: | edsarx.2304.12069 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |