Evaluation of shadow classification techniques for object detection and tracking
In a football stadium environment with multiple overhead floodlights, many protruding shadows can be observed originating from each of the targets. To track individual targets successfully, it is essential to achieve an accurate representation of the foreground. Many existing techniques are sensitive to shadows, falsely classifying shadows as foreground. The paper presents four different techniques associated with shadow classification. Three of the classifiers originate from the review material whilst the fourth is a novel application of a real-time implementation of the k-nearest neighbour algorithm to shadow identification. To assess the performance for each of the classifiers, four quantitative evaluation metrics are proposed. Using each of the evaluation metrics, we discuss the performance of each classifier's segmentation results and assess their impact on the tracking performance.