Abstract
This paper presents an effective, novel and robust framework for semantic analysis of a soccer video possessing varying illumination conditions. The proposed algorithm works in two phases. The proposed framework effectively detects and gathers important events in the first phase, and a later phase carries out the task of event classification. The proposed system aims to identify high-level semantics of a soccer video like card event, goal event, goal attack and other classes of events. The proposed framework effectively exploits optical flow and colour features to detect and classify the events. The use of event filtration and categorization features successfully makes the system effective over various conditions. Simulations have been performed on a large number of video datasets of different conditions of various soccer leagues. Simulation results reflect the efficiency and robustness of the proposed framework.
Highlights
Rapid revolution in digital video has brought many applications at home in affordable cost
We propose a robust and efficient framework for semantic analysis of a soccer video possessing highly varying illumination conditions
We propose a novel algorithm for event segmentation based on change in optical flow between the successive frames of the video
Summary
Rapid revolution in digital video has brought many applications at home in affordable cost. The volume of digital data has been increasing rapidly due to the wide usage of multimedia applications in the areas of education, entertainment, business, medicine etc. One of the major applications is sports video analysis. Sports videos attract majority of people due to their capability of producing thrill as well as uncertainty of results. There has been an enormous increase of such video contents on the Internet. Video summarization helps to address these needs by developing a condensed version of full-length videos [1,2,3]. Extraction of important events and creation of summaries do make the video compact and make it possible to deliver over lowbandwidth networks
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