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Event detection with vector similarity based on fourier transformation

  • Beihang University

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

Abstract

Event detection through sensors data recording human activities is an aspect to learn human behaviors. In this paper, counted numbers from a sensor installed on a building entrance recording the number of people entering the building, will be processed to find the anomaly time interval when there are more people going through the entrance, which is viewed as event. An approach is adopted having two steps: first, the counted numbers over time is processed by Fourier Transformation and we get the parameter of a vector (ReX[k], ImX[k]) representing kth point in the data set; second, the vectors of (ReX[k], ImX[k]) are classified by KNN algorithm in two dimensions, categorizing the data in the same time interval in 70 days and the data in 48 intervals in one day. The results show that the proposed method works well.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Control Science and Systems Engineering, CCSSE 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages195-199
Number of pages5
ISBN (Electronic)9781479963966
DOIs
StatePublished - 25 Aug 2015
EventIEEE International Conference on Control Science and Systems Engineering, CCSSE 2014 - Yantai, China
Duration: 29 Dec 201430 Dec 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Control Science and Systems Engineering, CCSSE 2014

Conference

ConferenceIEEE International Conference on Control Science and Systems Engineering, CCSSE 2014
Country/TerritoryChina
CityYantai
Period29/12/1430/12/14

Keywords

  • Fourier Transformation
  • KNN
  • event detection
  • vector similarity

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