Soccer line mark segmentation and classification with stochastic watershed transform

Description

This site contains some supplementary material associated to the detection strategy proposed in [1].

The work in [1] describes a strategy to automatically and accurately segment and classify line markings. First, line points are segmented thanks to a stochastic watershed transform that is robust to radial distortions, since it makes no assumptions about line straightness, and is unaffected by the presence of players or the ball. The line points are then linked to primitive structures (straight lines and ellipses) thanks to a very efficient procedure that makes no assumptions about the number of primitives that appear in each image.

The strategy has been tested on the LaSoDa database, a new and public database composed by 60 annotated images from matches in five stadiums. The results obtained have proven that the proposed strategy is more robust and accurate than existing approaches, achieving successful line mark detection even in challenging conditions.

Citation

[1] D. Berjón, C. Cuevas, and N. García, “Soccer line mark segmentation and classification with stochastic watershed transform", Signal Processing: Image Communication, under review.