BusyHands: A Hand-Tool Interaction Database for Assembly Tasks Semantic Segmentation
This work addresses the lack of datasets for hand-tool interaction segmentation, which is crucial for applications in robotics and human-computer interaction, but it is incremental as it primarily provides a new dataset rather than a novel method.
The authors tackled the problem of segmenting human hands during complex assembly tasks by introducing BusyHands, a large open dataset with 7906 pixel-level annotated images of hands performing 13 tool-based assembly tasks, including both real-world captures and virtual-world renderings, and evaluated state-of-the-art semantic segmentation methods on it as a benchmark.
Visual segmentation has seen tremendous advancement recently with ready solutions for a wide variety of scene types, including human hands and other body parts. However, focus on segmentation of human hands while performing complex tasks, such as manual assembly, is still severely lacking. Segmenting hands from tools, work pieces, background and other body parts is extremely difficult because of self-occlusions and intricate hand grips and poses. In this paper we introduce BusyHands, a large open dataset of pixel-level annotated images of hands performing 13 different tool-based assembly tasks, from both real-world captures and virtual-world renderings. A total of 7906 samples are included in our first-in-kind dataset, with both RGB and depth images as obtained from a Kinect V2 camera and Blender. We evaluate several state-of-the-art semantic segmentation methods on our dataset as a proposed performance benchmark.