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MRF Model for Detecting Abnormal Activates in Crowded Environments


K. Poomala, J. Jayageetha
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This paper focus on detecting unusual activities in video. The analysis of motions and behaviours in crowded scenes constitutes a challenging task for traditional computer vision methods. To overcome this disadvantage there are different methods are used to detect the abnormalities in the video. This proposed method shows that a space-time MRF (Markov Random Field) model for detecting abnormal activities like bicycle passing through a crowd. This method not only localizes abnormal activities in crowded scenes, it can also capture the irregular interactions between local activities in a global sense. Histograms of Oriented Gradients (HOG) are used for capture the image from the particular video. The extraction of appearance characteristics in Region Of Interest (ROI) tracked over time using HOG descriptor. The robustness of this method in practical application can be understood by applying it on long surveillance videos.

K. Poomala, J. Jayageetha


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Publication Details

Published in : Volume 2 | Issue 2 | March-April - 2016
Date of Publication Print ISSN Online ISSN
2016-04-25 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
112-115 IJSRSET162228   Technoscience Academy

Cite This Article

K. Poomala, J. Jayageetha, "MRF Model for Detecting Abnormal Activates in Crowded Environments", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.112-115, March-April-2016.
URL : http://ijsrset.com/IJSRSET162228.php