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We introduce a new large-scale data set of video URLs with densely-sampledobject bounding box annotations called YouTube-BoundingBoxes (YT-BB). The dataset consists of approximately 380,000 video segments about 19s long,automatically selected to feature objects in natural settings without editingor post-processing, with a recording quality often akin to that of a hand-heldcell phone camera. The objects represent a subset of the MS COCO label set. Allvideo segments were human-annotated with high-precision classification labelsand bounding boxes at 1 frame per second. The use of a cascade of increasinglyprecise human annotations ensures a label accuracy above 95% for every classand tight bounding boxes. Finally, we train and evaluate well-known deepnetwork architectures and report baseline figures for per-frame classificationand localization to provide a point of comparison for future work. We alsodemonstrate how the temporal contiguity of video can potentially be used toimprove such inferences. Please see the PDF file to find the URL to downloadthe data. We hope the availability of such large curated corpus will spur newadvances in video object detection and tracking.

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