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dc.contributor.authorSilambarasi, R-
dc.contributor.authorSahoo, Suraj Prakash-
dc.contributor.authorAri, Samit-
dc.identifier.citation6th IEEE International Conference on Communication and Signal Processing-ICCSP'17, Adhiparasakthi Engineering College, Melmaruvathur, Tamil Nadu, India, 4-6 April 2017en_US
dc.descriptionCopyright for this paper belongs to proceeding publisheren_US
dc.description.abstractThe paper presents an extended approach of Motion History Image to trace the human motions in a video for recognizing the human actions. The video is represented as a 3D volume space and the trace of human motions are projected onto the three different views called 3D spatio-temporal plane. The extended view traces both the human shape and movement in different directions over the time. The Histogram of Oriented Gradients (HOG) features are extracted over all the projection plane which gives more distinct featu es for the action classification. Since HOG features data has high dimensionality, the optimal featuresubset is selected by using the feature selection techniques. Finally, the Support Vector Machine (SVM) of multi-class classifier is used to identify the various actions of human. The various experiments are conducted on benchmark dataset KTH and results shows that the proposed method improves the action recognition rate compared to the existing methods.en_US
dc.subjectHuman Action Recognitionen_US
dc.subject3D Spatio-Temporal Planeen_US
dc.subjectMotion History Imagen_US
dc.subjectFeature Selectionen_US
dc.subjectSVM Classificationen_US
dc.title3D Spatial-Temporal View Based Motion Tracing in Human Action Recognitionen_US
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