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dc.contributor.author Chathuramali, KGM
dc.contributor.author Ramasinghe, S
dc.contributor.author Rodrigo, BKRP
dc.date.accessioned 2018-11-07T21:00:57Z
dc.date.available 2018-11-07T21:00:57Z
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/13654
dc.description.abstract Abnormal activity detection plays an important role in many areas such as surveillance, military installations, and sports. Existing abnormal activity detectors mostly rely on motion data obtained over a number of frames to characterize abnormality. However, only motion may not be able to capture all forms of abnormality, in particular, poses that do not amount to motion “outliers”. In this paper, we propose two different spatiotemporal descriptors, a silhouette and optic flow based method and a dense trajectory based method which additionally include trajectory shape descriptor, to detect abnormalities. These two descriptors enable us to classify abnormal versus non-abnormal activities using SVM. Comparison with existing methods, using five standard datasets, shows that dense trajectory based method outperforms state-of-the-art results in crowd dataset and silhouette and optic flow based method outperforms others in some datasets. en_US
dc.language.iso en en_US
dc.subject Abnormal activity detection, dense trajectories, HOG, HOF, MBH, SVM en_US
dc.title Abnormal activity recognition using spatio-temporal features en_US
dc.type Conference-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Electronic and Telecommunication Engineering en_US
dc.identifier.year 2014 en_US
dc.identifier.conference 7th International Conference on Information and Automation for Sustainability en_US
dc.identifier.place Colombo en_US
dc.identifier.email manosha@ent.mrt.ac.lk en_US
dc.identifier.email samramasinghe@gmail.com en_US
dc.identifier.email ranga@uom.lk en_US


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