1 research outputs found
Soccer line mark segmentation and classification with stochastic watershed transform
Augmented reality applications are beginning to change the way sports are
broadcast, providing richer experiences and valuable insights to fans. The
first step of augmented reality systems is camera calibration, possibly based
on detecting the line markings of the playing field. Most existing proposals
for line detection rely on edge detection and Hough transform, but radial
distortion and extraneous edges cause inaccurate or spurious detections of line
markings. We propose a novel 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 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.Comment: 18 pages, 11 figure