APIDIS European project led to two acquisitions of basketball games. The APIDIS dataset (presented here) and the SPIROUDOME dataset.

Download

  • Part 1 : raw video files for cameras 1 to 4.
  • Part 2 : raw video files for cameras 5 to 8.
  • Metadata : annotations, pseudo-synchronised dataset, code, …

Data

Approximatively 2 hours of videos were captured from 7 viewpoints during a professional basketball game.

Raw data

The cameras were recording at almost 22 fps in average, with a resolution of 1600x1200 pixels. The video files are available in their native format, i.e. one motion jpeg file (~300 MB) per minute per camera.

Pseudo-synchronised video

A pseudo synchronised dataset is provided by resampling the raw videos at 25 fps (using the closest available frame) with a resolution of 800x600 pixels. The video files are available as one MPEG-4 file (between 28 MB and 56 MB) per minute per camera. For optimal timestamps accuracy, the original dataset should be prefered.

Annotations

Timestamps

All timestamps are expressed in seconds since Epoch when provided as integers. When provided in a human readable format, e.g. in filenames, they follow the ISO 8601 date/time syntax.

Calibration

All cameras are Arecont Vision AV2100M IP cameras (datasheet). The fish-eye lenses used for the top view cameras are Fujinon FE185C086HA-1 lenses (datasheet). Each camera was indivually calibrated into a common world coordinate system.

Additional annotations

Several manually annotated objects are provided (see NEM'08 summit paper):

  • Basket ball events like ball possession periods, throws, violations ; for the whole basketball game
  • Ball, Players and referees position ; for one minute of the game.

In addition to ball annotation in the 2D images, an approximate 3D localization is inferred for the pseudo-synchronized dataset.

Terms of use

This dataset is available for non-commercial research in video signal processing only. We kindly ask you to mention the APIDIS project when using this dataset (in publications, video demonstrations…).

Acknowledgements

We would like to thank Jean-François Prior (Dexia Namur basket ball team), Philippe Delmulle Declercq Stortbeton Waregem basket ball team) and the city of Namur for their authorisations and technical help collecting this dataset.

Related publications

  • Parisot, Pascaline ; De Vleeschouwer, Christophe. Graph-based filtering of ballistic trajectory. IEEE International Conference on Multimedia and Expo (ICME), Barcelona, July 2011.
  • K.C., Amit Kumar ; Parisot, Pascaline ; De Vleeschouwer, Christophe. Spatio-temporal template matching for ball detection. ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), Ghent, Belgium, August 2011.
  • Parisot, Pascaline; De Vleeschouwer, Christophe. “Consensus-based trajectory estimation for ball detection in a calibrated cameras system.” (submitted to the Journal of Real-Time Image Processing) (code available in metadata part)
  • Delannay, Damien ; Danhier, Nicolas ; De Vleeschouwer, Christophe. Detection and recognition of sports(wo)men from multiple views. ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), Como, Italy, August 2009.
  • K.C., Amit Kumar ; De Vleeschouwer, Christophe. Discriminative Label Propagation for Multi-Object Tracking with Sporadic Appearance Features. International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013
  • Parisot, Pascaline ; Sevilmis, Berk ; De Vleeschouwer, Christophe. Training with corrupted labels to reinforce a probably correct teamsport player detector. Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science Volume 8192, 2013.
  • Parisot, Pascaline ; De Vleeschouwer, Christophe. Scene-specific classifier for effective and efficient team sport players detection from a single calibrated camera (in submission at CVIU - Special Issue on Computer Vision in Sports) (databases available in the metadata part of the dataset)
  • Chen, Fan ; De Vleeschouwer, Christophe. Personalized production of basketball videos from multi-sensored data under limited display resolution. Computer Vision and Image Understanding, Vol. 114, no. 6, p. 667-680, 2010.
  • De Vleeschouwer, Christophe ; Chen, Fan ; Delannay, Damien ; Parisot, Christophe ; Chaudy, Christophe ; Martrou, Eric ; Cavallaro, Andrea. Distributed video acquisition and annotation for sport-event summarization, NEM Summit, Saint Malo, France, 2008.
  • Chen, Fan ; Delannay, Damien ; De Vleeschouwer, Christophe. An Autonomous Framework to Produce and Distribute Personalized Team-Sport Video Summaries: A Basketball Case Study, IEEE Transactions on Multimedia, Volume:13 , Issue: 6, pp. 1381 - 1394, December 2011.

Contacts

If necessary, please contact the coordinator: Christophe De Vleeschouwer, christophe.devleeschouwer@uclouvain.be.
You can also contact Damien Delannay, Damien.Delannay@uclouvain.be.