YouTube-Objects dataset v2.2

Alessandro Prest, Vicky KalogeitonChristian LeistnerJavier CiveraCordelia Schmid, Vittorio Ferrari
University of Edinburgh (CALVIN), INRIA Grenoble (LEAR), ETH Zurich (CALVIN)

Overview

The YouTube-Objects dataset is composed of videos collected from YouTube by querying for the names of 10 object classes of the PASCAL VOC Challenge. It contains between 9 and 24 videos for each class. The duration of each video varies between 30 seconds and 3 minutes. The videos are weakly annotated, i.e. we ensure that each video contains at least one object of the corresponding class.

Dataset release v2.2

In this release, we improved the quality of the images by fixing some decompression problems. We also have better shot boundaries and we have annotated more bounding boxes (6,975) than the ones contained in v1.0 (1,407).

The dataset contains a total of 720,000 frames. In order to eliminate possible confusion when decoding the videos and in the frame numbering, we release individual video frames after decompression.

This release includes almost 7,000 bounding-box annotations. For evaluation purposes we divided the annotated frames into training and test sets. In the training set, we annotated one instance per frame, while in the test set we annotated all instances of the desired object class.

In addition to the videos and the bounding-box annotations, this release also includes:

  • Original videos with the audio tracks.
  • Optical flow, as produced by [4].
  • Superpixels, as produced by [5].

Important Notice

These videos were downloaded from the internet, and may subject to copyright. We don’t own the copyright of the videos and only provide them for non-commercial research purposes.

Downloads v2.2

Filename Description Release Date Size
README.txt Description of contents 1 January 2015 6.0KB
Ranges.tar.gz Videos and Shots of the dataset 1 January 2015 13.0KB
aeroplane.tar.gz 1 January 2015 1.8GB
bird.tar.gz 1 January 2015 3.0GB
boat.tar.gz 1 January 2015 11.0GB
car.tar.gz 1 January 2015 2.9GB
cat.tar.gz 1 January 2015 5.4GB
cow.tar.gz 1 January 2015 3.1GB
dog.tar.gz 1 January 2015 11.0GB
horse.tar.gz 1 January 2015 8.1GB
motorbike.tar.gz 1 January 2015 6.0GB
train.tar.gz 1 January 2015 11.0GB
GroundTruth.tar.gz Ground truth annotations 7 April 2015 91.0KB
OpticalFlow.tar.gz Optical flow by [4] 7 April 2015 4.3GB
SlicSuperpixels.tar.gz Superpixels by [5] 17 April 2015 16.7GB
UsefulFiles.tar.gz Useful files for the dataset 1 January 2015 27.0MB
YouTubeObjectsVideos.tar.gz Videos (including audios) 1 January 2015 6.3GB

Dataset release v1.0

This release contains a total of 570’000 frames. As demonstrated in [1], the quality of the video frames play a crucial role in the performance of an object detector trained on them. We release individual video frames after decompression and after shot partitioning. In this manner, you are in possession of a perfect copy of the dataset as we used in our experiments [1].

In addition to the videos, this release also includes several materials from our paper [1]

  • Bounding-boxes annotations. For evaluation purposes we annotated the object location in a few hundred video frames for each class (see sec. 6.1 [1]).
  • Point tracks and motion segments. As produced by [2].
  • Tubes. Spatio-temporal bounding-boxes as described in section 3.2 [1]. We include all candidate tubes as well as the tube automatically selected by our method.

Downloads v1.0

Filename Description Release Date Size
code.tar.gz MATLAB source code to access the Youtube-Objects dataset. 17 June 2012 1MB
aeroplane.tar.gz 17 June 2012 2.0GB
bird.tar.gz 17 June 2012 3.0GB
boat.tar.gz 17 June 2012 7.6GB
car.tar.gz 17 June 2012 1.7GB
cat.tar.gz 17 June 2012 5.2GB
cow.tar.gz 17 June 2012 6.1GB
dog.tar.gz 17 June 2012 19.5GB
horse.tar.gz 17 June 2012 14.7GB
motorbike.tar.gz 17 June 2012 4.3GB
train.tar.gz 17 June 2012 21.1GB

References

  1. Learning Object Class Detectors from Weakly Annotated Video
    Alessandro PrestChristian LeistnerJavier CiveraCordelia SchmidVittorio Ferrari,
    In Computer Vision and Pattern Recognition (CVPR), 2012.
    [bib][pdf][url]
  2. Object segmentation by long term analysis of point trajectories
    T. Brox, J. Malik,
    In European Conference on Computer Vision (ECCV), 2010.
    [pdf]
  3. Analysing domain shift factors between videos and images for object detection
    Vicky Kalogeiton, Vittorio Ferrari, Cordelia Schmid,
    In arXiv preprint, 2015.
    [pdf]
  4. Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
    Thomas Brox and Jitendra Malik
    In PAMI , 2011.
  5. SLIC Superpixels Compared to State-of-the-art Superpixel Methods
    Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk
    In PAMI , 2012.

Acknowledgements

This work was partially funded by the QUAERO project supported by OSEO, French State agency for innovation, the European integrated projects AXES and RoboEarth, DPI2009-07130, SNSF IZK0Z2-136096, CAIDGA IT 26/10, a Google Research Award and the ERC projects VisCul and ALLEGRO.