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.

Attached Files
Name Description MIMEType Size Downloads

Title Semantic trajectory based event detection and event pattern mining
Author(s) Wang, Xiaofeng
Li, Gang
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
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
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Google Scholar Search Google Scholar
Access Statistics: 78 Abstract Views, 6 File Downloads  -  Detailed Statistics
Created: Thu, 05 Apr 2012, 15:51:00 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.