YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video

TL;DR

This paper introduces YouTube-BoundingBoxes, a large-scale, high-precision video dataset with dense object annotations, aiming to advance research in video object detection and tracking.

Contribution

The creation of a large, high-quality, human-annotated video dataset with dense bounding boxes and classification labels for object detection research.

Findings

Dataset contains approximately 380,000 video segments.

High annotation accuracy above 95%.

Baseline results for deep network architectures.

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 MS COCO 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. Please see the PDF file to find the URL to download the data. We hope the availability of such large curated corpus will spur new advances in video object detection and tracking.