HUMAN MOVEMENT DETECTION DENGAN ACCUMULATIVE DIFFERENCES IMAGE

  • Mohammad Faisal Kholid Jurusan Ilmu Komputer Universitas Bumigora
  • Jian Budiarto Jurusan Ilmu Komputer Universitas Bumigora
  • Ahmad Ashril Rizal STMIK Syaikh Zainuddin NW Anjani
  • Gibran Satya Nugraha Program Studi Teknik Informatika Universitas Mataram
Keywords: Motion Detection, ADI, Haar Cascade, Motion Detection, Accumulative difference image, Human Detection, Open Cv

Abstract

Based on police data quoted from one of the online news portal pages, there are 43,842 thousand criminal acts in the The Capital City of Jakarta. Of all these criminal cases burglary empty houses included in the top three acts of crime. Houses that are abandoned by their owners are often targeted by crime operations due to lack of close supervision and security support technology. The purpose of this study is to detect human motion which can later be used to prevent crime in the form of theft. Another purpose of this research is to find out how the method used works in identifying changes in the image of several consecutive frames. This research develops a motion detection system in humans on video using a Closed Circuit Television (CCTV) camera which is simulated using sample video. The motion detection process uses the Accumulative Differences Image (ADI) method and the human detection process uses the classification of Opencv, the Haar Cascade Classification. Which with this method compares more than two different frames and the classification parameters used are full-body, upper body and face. System testing is done using several video samples taken with the distance and height of the camera against different objects. The results obtained from testing using video samples show an accuracy rate of 95.23%.

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Published
2020-05-23
How to Cite
Mohammad Faisal Kholid, Jian Budiarto, Ahmad Ashril Rizal, & Gibran Satya Nugraha. (2020). HUMAN MOVEMENT DETECTION DENGAN ACCUMULATIVE DIFFERENCES IMAGE. TEKNIMEDIA: Teknologi Informasi Dan Multimedia, 1(1), 1-7. https://doi.org/10.46764/teknimedia.v1i1.7
Section
Articles
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