{'fraction_non_alphanumeric': 0.02034174125305126, 'fraction_numerical': 0.0, 'mean_word_length': 5.15, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Accurate playfield detection using Area-of-Coverage

Playfield detection is an important task in sports video content analysis, as it provides the ground for further operations such as object detection, object tracking or semantic event highlight and summarization. Traditional approaches of playfield detection usually rely on the homogeneity cue of the field pixels' color to separate it from other areas within the image frame. Although encouraging results have been achieved using this methodology, the accuracy of the detection still largely varies depending on the grass pattern of the field. In this paper, we propose a novel approach to detect the playfield region using the concept of Area-of-Coverage. First, the system uses Gaussian Mixture Model (GMM) to obtain a rough segmentation of the playfield area. Based on this initial segmentation, feature points of the field are detected for calibration using homography and then the Area of Coverage (AoC) of the camera view is calculated. Finally, this AoC is back-projected to the image frame to provide a refine playfield detection. Experiments on real videos show that the proposed system achieves higher accuracy as well as stability compared to existing approaches.