Smartphones and tablets promote a personalized consumption of video content (De Vleeschouwer 2011) , making the democratic production of targeted content an exciting challenge for content providers. This research builds on computer vision tools to face this challenge in controlled scenarios, as most notably encountered for sport events.

As depicted in the figure below, the automatic production is in charge of two main decisions, namely camera selection and image cropping parameters.

In short, the production decisions in a static-camera infrastructure are derived from player (and ball) detection tools (Parisot et al. 2013) , so as to optimize a trade-off between three factors:

  • Completeness counts the number of salient objects in the scene, and thereby measures the integrity of the camera/viewpoint selection;
  • Fineness refers to the amount of details provided about the rendered scene. It is measured in terms of pixel resolution of the salient objects, and thereby favors close views selection. Increasing the fineness of a video does not only improve the viewing experience, but is also essential in guiding the emotional involvement of viewers through the use of close-up shots.
  • Smoothness refers to the graceful displacement of the camera viewpoint, and to the continuous story-telling resulting from the camera switching. Preserving smoothness is important to avoid distracting the viewer from the story with abrupt changes of viewpoint.


Based on the related scientific contributions (Chen et al. 2011) , (Chen and De Vleeschouwer 2010) , a fully automatic sport production solution has been developed within a European research project (www.apidis.org). The solution is now commercialized by Keemotion (www.keemotion.com) to cover team-sport events in a cost-effective manner, making the creation of video reports affordable, even in case of small or medium-size audience.

The following figures present two image samples resulting from the patented technology developed in those projects. In the first image, the technology is used to decide how to crop images in a panoramic view of the basket-ball field. In the second image, the production decisions include both camera selection and definition of image cropping parameters, as a function of the scene at hand.