Active Foreground Region Extraction and Tracking for Sports Video Annotation

• Published in: Neural processing letters Vol. 37; no. 1; pp. 33 - 46
• Main Authors: Mentzelopoulos, Markos, Psarrou, Alexandra, Angelopoulou, Anastassia, García-Rodríguez, José
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.02.2013; Springer; Springer Nature B.V
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Boundaries; Cameras; Classification; Clustering; Complex Systems; Computational Intelligence; Computer Science; Computer science; control theory; systems; Entropy; Exact sciences and technology; Image annotation; Image processing; Methods; Pattern recognition. Digital image processing. Computational geometry; Relativity; Segmentation; Semantics; Tracking; Video; Sports video; Background subtraction; Parametric and non-parametric approaches; Spatial correlation; Object detection; Clustering; Dominant color; Tracking; Image processing; High precision; Active region; Video signal; Activity; Modeling; Sport; Efficiency; Classification; Computer vision; Motion estimation; Cluster; Object recognition; Annotation; Image segmentation; Experimental result; Subtraction; Automatic measurement; Scene analysis; Edge detection; Algorithm analysis
• DOI: 10.1007/s11063-012-9267-4
• ISSN: 1370-4621

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.