Semantic trajectory based event detection and event pattern mining Wang, Xiaofeng, Li, Gang, Jiang, Guang and Shi, Zhongzhi 2011, Semantic trajectory based event detection and event pattern mining, Knowledge and information systems, vol. 37, pp. 305-329, doi: 10.1007/s10115-011-0471-8. Attached Files Name Description MIMEType Size Downloads Title Semantic trajectory based event detection and event pattern mining Author(s) Wang, Xiaofeng Li, Gang orcid.org/0000-0003-1583-641X Jiang, Guang Shi, Zhongzhi Journal name Knowledge and information systems Volume number 37 Start page 305 End page 329 Total pages 25 Publisher Springer UK Place of publication London, England Publication date 2011-12 ISSN 0219-1377 0219-3116 Keyword(s) video event detection ontology reasoning frequent pattern mining PrefixSpan Summary Video event detection is an effective way to automatically understand the semantic content of the video. However, due to the mismatch between low-level visual features and high-level semantics, the research of video event detection encounters a number of challenges, such as how to extract the suitable information from video, how to represent the event, how to build up reasoning mechanism to infer the event according to video information. In this paper, we propose a novel event detection method. The method detects the video event based on the semantic trajectory, which is a high-level semantic description of the moving object’s trajectory in the video. The proposed method consists of three phases to transform low-level visual features to middle-level raw trajectory information and then to high-level semantic trajectory information. Event reasoning is then carried out with the assistance of semantic trajectory information and background knowledge. Additionally, to release the users’ burden in manual event definition, a method is further proposed to automatically discover the event-related semantic trajectory pattern from the sample semantic trajectories. Furthermore, in order to effectively use the discovered semantic trajectory patterns, the associative classification-based event detection framework is adopted to discover the possibly occurred event. Empirical studies show our methods can effectively and efficiently detect video events. Language eng DOI 10.1007/s10115-011-0471-8 Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2011, Springer-Verlag London Limited Persistent URL http://hdl.handle.net/10536/DRO/DU:30044128 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology 2018 ERA Submission Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 05 Apr 2012, 15:51:00 EST Wed, 18 Apr 2012, 16:07:01 EST Wed, 18 Apr 2012, 16:24:39 EST Thu, 31 May 2012, 16:10:06 EST Mon, 04 Nov 2013, 13:16:20 EST Tue, 05 Nov 2013, 08:06:56 EST Tue, 05 Nov 2013, 08:09:30 EST Fri, 13 Jun 2014, 13:29:52 EST Fri, 17 Oct 2014, 11:26:43 EST Tue, 03 Feb 2015, 13:03:22 EST Fri, 09 Feb 2018, 11:03:34 EST Filtered Full Citation counts:   Cited 12 times in TR Web of Science Cited 18 times in Scopus Search Google Scholar Access Statistics: 667 Abstract Views, 11 File Downloads  -  Detailed Statistics Created: Thu, 05 Apr 2012, 15:51:00 EST

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