Comparing Video Activity Classifiers within a Novel Framework
Keyword(s):
Video activity classification has many applications. It is challenging because of the diverse characteristics of different events. In this paper, we examined different approaches to event classification within a general framework for video activity detection and classification. In our experiments, we focused on event classification in which we explored a deep learning-based approach, a rule-based approach, and a hybrid combination of the previous two approaches. Experimental results using the well-known Video Image Retrieval and Analysis Tool (VIRAT) database showed that the proposed classification approaches within the framework are promising and more research is needed in this area
2020 ◽
Vol ahead-of-print
(ahead-of-print)
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Keyword(s):
2018 ◽
Vol 8
(1)
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pp. 14
2019 ◽
Vol 50
(2)
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pp. 98-112
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Keyword(s):
2010 ◽
Vol 12
(1)
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pp. 9-16
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