Activity analysis based on SOM

Xiu Xiu Li, Jiang Bin Zheng, Yan Ning Zhang, He Jin Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper presents a novel SOM algorithm to classify the moving objects activities according to their trajectories. Firstly, a trajectory is represented as a sequence of vector that consists of the temporal motion feature and predictive motion information of moving object, and a good classification result benefits from the predictive motion information. Secondly, the motion features and predictive information of normal trajectories are learnt by a SOM network, and a SOM network is constructed to pattern the similarity of normal moving trajectories. Finally, this SOM network is used to classify the normal or abnormal trajectories of the moving objects by detecting abnormal points of trajectories, especially at the exact moment once the abnormal activity occurs. Experiments show that the proposed algorithm is effective.

Original languageEnglish
Title of host publicationProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages3975-3979
Number of pages5
DOIs
StatePublished - 2007
Event6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China
Duration: 19 Aug 200722 Aug 2007

Publication series

NameProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume7

Conference

Conference6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
Country/TerritoryChina
CityHong Kong
Period19/08/0722/08/07

Keywords

  • Predictive information
  • SOM
  • Trajectory classify

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