  Procedural video understanding is gaining attention in the vision and
language community. Deep learning-based video analysis requires extensive data.
Consequently, existing works often use web videos as training resources, making
it challenging to query instructional contents from raw video observations. To
address this issue, we propose a new dataset, COM Kitchens. The dataset
consists of unedited overhead-view videos captured by smartphones, in which
participants performed food preparation based on given recipes. Fixed-viewpoint
video datasets often lack environmental diversity due to high camera setup
costs. We used modern wide-angle smartphone lenses to cover cooking counters
from sink to cooktop in an overhead view, capturing activity without in-person
assistance. With this setup, we collected a diverse dataset by distributing
smartphones to participants. With this dataset, we propose the novel
video-to-text retrieval task Online Recipe Retrieval (OnRR) and new video
captioning domain Dense Video Captioning on unedited Overhead-View videos
(DVC-OV). Our experiments verified the capabilities and limitations of current
web-video-based SOTA methods in handling these tasks.
