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Shadow Detection for Vehicle Detection in Urban Environments

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorHanif, Muhammad
dc.contributor.authorHussain, Fawad
dc.contributor.authorYousaf, Muhammad Haroon
dc.contributor.authorVelastin Carroza, Sergio Alejandro
dc.contributor.authorChen, Zezhi
dc.date.accessioned2019-09-30T10:34:45Z
dc.date.available2019-09-30T10:34:45Z
dc.date.issued2017-07-02
dc.descriptionThis paper has been presented at : 14th International Conference on Image Analysis and Recognition (ICIAR 2017)en
dc.description.abstractFinding an accurate and computationally efficient vehicle detection and classification algorithm for urban environment is challenging due to large video datasets and complexity of the task. Many algorithms have been proposed but there is no efficient algorithm due to various real-time issues. This paper proposes an algorithm which addresses shadow detection (which causes vehicles misdetection and misclassification) and incorporates solution of other challenges such as camera vibration, blurred image, illumination and weather changing effects. For accurate vehicles detection and classification, a combination of self-adaptive GMM and multi-dimensional Gaussian density transform has been used for modeling the distribution of color image data. RGB and HSV color space based shadow detection is proposed. Measurement-based feature and intensity based pyramid histogram of orientation gradient are used for classification into four main vehicle categories. The proposed method achieved 96.39% accuracy, while tested on Chile (MTT) dataset recorded at different times and weather conditions and hence suitable for urban traffic environmenten
dc.format.extent11
dc.identifier.bibliographicCitationHanif, M., Hussain, F., Yousaf, M.H., Velastin, S. A. y Chen, Z. (2017). Shadow Detection for Vehicle Classification in Urban Environments. In Image Analysis and Recognition, LNCS,10317, pp. 352-362.en
dc.identifier.doihttps://doi.org/10.1007/978-3-319-59876-5_39
dc.identifier.isbn978-3-319-59875-8
dc.identifier.publicationfirstpage352
dc.identifier.publicationlastpage362
dc.identifier.publicationtitleImage Analysis and Recognitionen
dc.identifier.publicationvolume10317
dc.identifier.urihttps://hdl.handle.net/10016/28922
dc.identifier.uxxiCC/0000027463
dc.language.isoengen
dc.publisherSpringeren
dc.relation.eventdate5-7 July 2017en
dc.relation.eventplaceMontreal, Canadaen
dc.relation.eventtitle14th International Conference on Image Analysis and Recognition ICIAR 2017en
dc.rights© Springer International Publishing AG 2017en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherGaussian mixture modelen
dc.subject.otherHistogram of oriented gradienten
dc.subject.otherITSen
dc.subject.otherRGBen
dc.subject.otherHSVen
dc.subject.otherInter-channel intensity deviationen
dc.titleShadow Detection for Vehicle Detection in Urban Environmentsen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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