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Issue No.03 - March (2012 vol.34)
pp: 615-621
L. Wolf , Blavatnik Sch. of Comput. Sci., Tel Aviv Univ., Tel Aviv, Israel
ABSTRACT
Recognizing actions in videos is rapidly becoming a topic of much research. To facilitate the development of methods for action recognition, several video collections, along with benchmark protocols, have previously been proposed. In this paper, we present a novel video database, the “Action Similarity LAbeliNg” (ASLAN) database, along with benchmark protocols. The ASLAN set includes thousands of videos collected from the web, in over 400 complex action classes. Our benchmark protocols focus on action similarity (same/not-same), rather than action classification, and testing is performed on never-before-seen actions. We propose this data set and benchmark as a means for gaining a more principled understanding of what makes actions different or similar, rather than learning the properties of particular action classes. We present baseline results on our benchmark, and compare them to human performance. To promote further study of action similarity techniques, we make the ASLAN database, benchmarks, and descriptor encodings publicly available to the research community.
INDEX TERMS
Web services, computer vision, pattern recognition, protocols, video databases, descriptor encodings, action recognition, video collections, benchmark protocols, video database, action similarity labeling, ASLAN database, Web service, action classes, Videos, Databases, Benchmark testing, YouTube, Training, Cameras, benchmark., Action recognition, action similarity, video database, web videos
CITATION
L. Wolf, "The Action Similarity Labeling Challenge", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 3, pp. 615-621, March 2012, doi:10.1109/TPAMI.2011.209
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