YouTube-BoundingBoxes: A Large High-Precision
Human-Annotated Data Set for Object Detection in Video
Abstract
We introduce a new large-scale data set of video URLs
with densely-sampled object bounding box annotations
called YouTube-BoundingBoxes (YT-BB). The data set consists of approximately 380,000 video segments about 19s
long, automatically selected to feature objects in natural
settings without editing or post-processing, with a recording quality often akin to that of a hand-held cell phone
camera. The objects represent a subset of the COCO [32]
label set. All video segments were human-annotated with
high-precision classification labels and bounding boxes at
1 frame per second. The use of a cascade of increasingly precise human annotations ensures a label accuracy
above 95% for every class and tight bounding boxes. Finally, we train and evaluate well-known deep network architectures and report baseline figures for per-frame classification and localization to provide a point of comparison for future work. We also demonstrate how the temporal contiguity of video can potentially be used to improve such inferences. The data set can be found at
https://research.google.com/youtube-bb. We hope the availability of such large curated corpus will spur new advances
in video object detection and tracking