We address the challenging task of identifying, segmenting, and tracking hand-held objects, which is crucial for applications such as human action segmentation and performance evaluation. This task is particularly challenging due to heavy occlusion, rapid motion, and the transitory nature of objects being hand-held, where an object may be held, released, and subsequently picked up again. To tackle these challenges, we have developed a novel transformer-based architecture called HOIST-Former. HOIST-Former is adept at spatially and temporally segmenting hands and objects by iteratively pooling features from each other, ensuring that the processes of identification, segmentation, and tracking of hand-held objects depend on the hands' positions and their contextual appearance. We further refine HOIST-Former with a contact loss that focuses on areas where hands are in contact with objects. Moreover, we also contribute an in-the-wild video dataset called HOIST, which comprises 4,125 videos complete with bounding boxes, segmentation masks, and tracking IDs for hand-held objects. Through experiments on the HOIST dataset and two additional public datasets, we demonstrate the efficacy of HOIST-Former in segmenting and tracking hand-held objects.

我们开发了一种名为HOIST-Former的新型基于Transformer的架构，通过迭代地从彼此汇集特征来空间和时间地分割手和物体，确保手持物体的识别、分割和跟踪过程依赖于手的位置和它们的上下文外观。我们通过在HOIST数据集和其他两个公开数据集上进行实验，证明了HOIST-Former在分割和跟踪手持物体方面的有效性。

野外手持物品识别、分割和追踪: HOIST-Former