YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video
arXiv:1702.00824
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.
Accepted at the Conference on Computer Vision and Pattern Recognition (CVPR) 2017
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- AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
- Unsupervised Deep Tracking
- Exploring Object Relation in Mean Teacher for Cross-Domain Detection
- Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking
- Multi-hierarchical Independent Correlation Filters for Visual Tracking
- Benchmarking Deep Trackers on Aerial Videos
- Improving Video Generation for Multi-functional Applications
- Deeply Activated Salient Region for Instance Search
- Learning Global Structure Consistency for Robust Object Tracking
- Hierarchical Transfer Convolutional Neural Networks for Image Classification
- Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation
- Exploring Speech Cues in Web-mined COVID-19 Conversational Vlogs
- Computer-Aided Annotation for Video Tampering Dataset of Forensic Research