Publication:
Bag of Deep Features for Instructor Activity Recognition in Lecture Room

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorNida, Nudrat
dc.contributor.authorYousaf, Muhammad Haroon
dc.contributor.authorIrtaza, Aun
dc.contributor.authorVelastin Carroza, Sergio Alejandro
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderMinisterio de Educación, Cultura y Deporte (España)es
dc.date.accessioned2019-09-26T10:02:53Z
dc.date.available2019-09-26T10:02:53Z
dc.date.issued2019-01
dc.descriptionThis paper has been presented at : 25th International Conference on MultiMedia Modeling (MMM2019)en
dc.description.abstractThis research aims to explore contextual visual information in the lecture room, to assist an instructor to articulate the effectiveness of the delivered lecture. The objective is to enable a self-evaluation mechanism for the instructor to improve lecture productivity by understanding their activities. Teacher’s effectiveness has a remarkable impact on uplifting students performance to make them succeed academically and professionally. Therefore, the process of lecture evaluation can significantly contribute to improve academic quality and governance. In this paper, we propose a vision-based framework to recognize the activities of the instructor for self-evaluation of the delivered lectures. The proposed approach uses motion templates of instructor activities and describes them through a Bag-of-Deep features (BoDF) representation. Deep spatio-temporal features extracted from motion templates are utilized to compile a visual vocabulary. The visual vocabulary for instructor activity recognition is quantized to optimize the learning model. A Support Vector Machine classifier is used to generate the model and predict the instructor activities. We evaluated the proposed scheme on a self-captured lecture room dataset, IAVID-1. Eight instructor activities: pointing towards the student, pointing towards board or screen, idle, interacting, sitting, walking, using a mobile phone and using a laptop, are recognized with an 85.41% accuracy. As a result, the proposed framework enables instructor activity recognition without human intervention.en
dc.description.sponsorshipSergio A Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2014-51509) el Ministerio de Educación, Cultura y Deporte (CEI-15-17) and Banco Santander.en
dc.format.extent12
dc.identifier.bibliographicCitationNida, N., Yousaf, M.H., Irtaza, A. y Velastin, S.A. (2019). Bag of Deep Features for Instructor Activity Recognition in Lecture Room. In MultiMedia Modeling,11296, pp. 481-492.en
dc.identifier.doihttps://doi.org/10.1007/978-3-030-05716-9_39
dc.identifier.isbn978-3-030-05715-2
dc.identifier.publicationfirstpage481
dc.identifier.publicationlastpage492
dc.identifier.publicationtitleMultiMedia Modelingen
dc.identifier.publicationvolume11296
dc.identifier.urihttps://hdl.handle.net/10016/28908
dc.identifier.uxxiCC/0000029976
dc.language.isoengen
dc.publisherSpringeren
dc.relation.eventdate08-11 January 2019en
dc.relation.eventplaceThessaloniki, Greeceen
dc.relation.eventtitle25th International Conference on MultiMedia Modeling (MMM2019)en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/600371en
dc.relation.projectIDGobierno de España. COFUND2013-51509es
dc.relation.projectIDGobierno de España. CEI-15-17es
dc.rights© Springer Nature Switzerland AG 2019en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherHuman activity recognitionen
dc.subject.otherInstructor activity recognitionen
dc.subject.otherMotion templatesen
dc.subject.otherAcademic quality assuranceen
dc.titleBag of Deep Features for Instructor Activity Recognition in Lecture Roomen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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