Classifying abnormal activities in exam using multi-class Markov chain LDA based on MODEC features

Janson Hendryli, Mohamad Ivan Fanany

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

4 Citations (Scopus)

Abstract

In this paper, we apply MCMCLDA (Multi-class Markov Chain Latent Dirichlet Allocation) model to classify abnormal activity of students in an examination. Abnormal activity in exams is defined as a cheating activity. We compare the usage of Harris3D interest point detector and a human joints detector, MODEC (Multimodal Decomposable Models), as the feature detector. Experiment results show that using MODEC to detect arm joints and head location as interest point gives better performance in accuracy and computational time than Harris3D when classifying cheating activity. MODEC suffers low accuracy due to its inability to differentiate elbow and wrist when the object wears clothes with indistinguishable colors. Meanwhile, Harris3D detects too many irrelevant interest point to recognize cheating activity reliably.

Original languageEnglish
Title of host publication2016 4th International Conference on Information and Communication Technology, ICoICT 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467398794
DOIs
Publication statusPublished - 19 Sep 2016
Event4th International Conference on Information and Communication Technology, ICoICT 2016 - Bandung, Indonesia
Duration: 25 May 201627 May 2016

Publication series

Name2016 4th International Conference on Information and Communication Technology, ICoICT 2016

Conference

Conference4th International Conference on Information and Communication Technology, ICoICT 2016
Country/TerritoryIndonesia
CityBandung
Period25/05/1627/05/16

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