Precision-Enhanced Human-Object Contact Detection via Depth-Aware Perspective Interaction and Object Texture Restoration
Journal:
arXiv
Published Date:
Dec 13, 2024
Abstract
Human-object contact (HOT) is designed to accurately identify the areas where
humans and objects come into contact. Current methods frequently fail to
account for scenarios where objects are frequently blocking the view, resulting
in inaccurate identification of contact areas. To tackle this problem, we
suggest using a perspective interaction HOT detector called PIHOT, which
utilizes a depth map generation model to offer depth information of humans and
objects related to the camera, thereby preventing false interaction detection.
Furthermore, we use mask dilatation and object restoration techniques to
restore the texture details in covered areas, improve the boundaries between
objects, and enhance the perception of humans interacting with objects.
Moreover, a spatial awareness perception is intended to concentrate on the
characteristic features close to the points of contact. The experimental
results show that the PIHOT algorithm achieves state-of-the-art performance on
three benchmark datasets for HOT detection tasks. Compared to the most recent
DHOT, our method enjoys an average improvement of 13%, 27.5%, 16%, and 18.5% on
SC-Acc., C-Acc., mIoU, and wIoU metrics, respectively.
