Shadow Classification and Evaluation for Soccer Player Detection

In a football stadium environment with multiple overhead floodlights, many protruding shadows can be observed originating from each of the targets. To successfully track individual targets, it is essential to achieve an accurate representation of the foreground. Unfortunately, many of the existing techniques are sensitive to shadows, falsely classifying them as foreground. In this work an unsupervised learning procedure that determines the RGB colour distributions of the foreground and shadow classes of feature data is proposed. A novel skelatonisation and spatial filtering process is developed for identifying components in the foreground segmentation that are most-likely to belong to each class of feature. A pixel classification mechanism is obtained at by approximating both classes of feature data by N Gaussian parametric models. To assess our technique’s performance and reliability, a comparison is made with other published works.