NDLI: Automatic detection of 'Goal' segments in basketball videos

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Automatic detection of 'Goal' segments in basketball videos

Content Provider	ACM Digital Library
Author	Nepal, Surya Srinivasan, Uma Reynolds, Graham
Abstract	Advances in the media and entertainment industries, for example streaming audio and digital TV, present new challenges for managing large audio-visual collections. Efficient and effective retrieval from large content collections forms an important component of the business models for content holders and this is driving a need for research in audio-visual search and retrieval. Current content management systems support retrieval using low-level features, such as motion, colour, texture, beat and loudness. However, low-level features often have little meaning for the human users of these systems, who much prefer to identify content using high-level semantic descriptions or concepts. This creates a gap between the system and the user that must be bridged for these systems to be used effectively. The research presented in this paper describes our approach to bridging this gap in a specific content domain, sports video. Our approach is based on a number of automatic techniques for feature detection used in combination with heuristic rules determined through manual observations of sports footage. This has led to a set of models for interesting sporting events-goal segments-that have been implemented as part of an information retrieval system. The paper also presents results comparing output of the system against manually identified goals.
Starting Page	261
Ending Page	269
Page Count	9
File Format	PDF
ISBN	1581133944
DOI	10.1145/500141.500181
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2001-10-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sports video analysis Temporal models Content-based retrieval
Content Type	Text
Resource Type	Article

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