Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

Multi-camera video surveillance for real-time analysis and reconstruction of soccer games

Ren, Jinchang and Xu, M. and Orwell, J. and Jones, G. (2010) Multi-camera video surveillance for real-time analysis and reconstruction of soccer games. Machine Vision and Applications, 21 (6). pp. 855-863.

[img]
Preview
PDF (RenXuOrwellJones-MVA2010-multi-camera-video)
mva_soccer_rev7.pdf - Preprint

Download (884kB) | Preview

Abstract

Soccer analysis and reconstruction is one of the most interesting challenges for wide-area video surveillance applications. Techniques and system implementation for tracking the ball and players with multiple stationary cameras are discussed. With video data captured from a football stadium, the real-world, real-time positions of the ball and players can be generated. The whole system contains a twostage workflow, i.e., single view and multi-view processing. The first stage includes categorizing of players and filtering of the ball after changing detection against an adaptive background and image-plane tracking. Occlusion reasoning and tracking-back is applied for robust ball filtering. In the multi-viewstage, multiple observations from overlapped single views are fused to refine players’ positions and to estimate 3-D ball positions using geometric constraints. Experimental results on real data from long sequences are demonstrated.