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An Optimized and Fast Scheme for Real-time Human Detection using Raspberry Pi

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorNoman, Mubashir
dc.contributor.authorYousaf, Muhammad Haroon
dc.contributor.authorVelastin Carroza, Sergio Alejandro
dc.date.accessioned2019-10-01T10:01:17Z
dc.date.available2019-10-01T10:01:17Z
dc.date.issued2016-11-30
dc.descriptionThis paper has been presented at : The International Conference on Digital Image Computing: Techniques and Applications (DICTA 2016)en
dc.description.abstractReal-time human detection is a challenging task due to appearance variance, occlusion and rapidly changing content; therefore it requires efficient hardware and optimized software. This paper presents a real-time human detection scheme on a Raspberry Pi. An efficient algorithm for human detection is proposed by processing regions of interest (ROI) based upon foreground estimation. Different number of scales have been considered for computing Histogram of Oriented Gradients (HOG) features for the selected ROI. Support vector machine (SVM) is employed for classification of HOG feature vectors into detected and non-detected human regions. Detected human regions are further filtered by analyzing the area of overlapping regions. Considering the limited capabilities of Raspberry Pi, the proposed scheme is evaluated using six different testing schemes on Town Centre and CAVIAR datasets. Out of these six testing schemes, Single Window with two Scales (SW2S) processes 3 frames per second with acceptable less accuracy than the original HOG. The proposed algorithm is about 8 times faster than the original multi-scale HOG and recommended to be used for real-time human detection on a Raspberry Pi.en
dc.format.extent7
dc.identifier.bibliographicCitationNoman, M., Yousaf, M.H., Velastin, S. A. (2016). An Optimized and Fast Scheme for Real-Time Human Detection Using Raspberry Pi. In: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).en
dc.identifier.doihttps://doi.org/10.1109/DICTA.2016.7797008
dc.identifier.isbn978-1-5090-2896-2
dc.identifier.publicationtitle2016 International Conference on Digital Image Computing: Techniques and Applicationsen
dc.identifier.urihttps://hdl.handle.net/10016/28934
dc.identifier.uxxiCC/0000027414
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate30 Nov.-2 Dec. 2016en
dc.relation.eventplaceGold Coast, QLD, Australiaen
dc.relation.eventtitle2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)en
dc.rights© 2016 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherHistogram of oriented gradients (Hog)en
dc.subject.otherRaspberry pien
dc.subject.otherTown centre dataseten
dc.subject.otherCaviar dataseten
dc.subject.otherSupport vectoren
dc.subject.otherMachineen
dc.subject.otherHuman detectionen
dc.titleAn Optimized and Fast Scheme for Real-time Human Detection using Raspberry Pien
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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