Classifying Pairwise Object Interactions: A Trajectory Analytics Approach

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We have a huge amount of video data from extensively available surveillance cameras and increasingly growing technology to record the motion of a moving object in the form of trajectory data. With proliferation of location-enabled devices and ongoing growth in smartphone penetration as well as advancements in exploiting image processing techniques, tracking moving objects is more flawlessly achievable. In this work, we explore some domain-independent qualitative and quantitative features in raw trajectory (spatio-temporal) data in videos captured by a fixed single wide-angle view camera sensor in outdoor areas. We study the efficacy of those features in classifying four basic high ... continued below

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vii, 27 pages : illustrations (some color)

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Janmohammadi, Siamak May 2015.

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  • Janmohammadi, Siamak

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We have a huge amount of video data from extensively available surveillance cameras and increasingly growing technology to record the motion of a moving object in the form of trajectory data. With proliferation of location-enabled devices and ongoing growth in smartphone penetration as well as advancements in exploiting image processing techniques, tracking moving objects is more flawlessly achievable. In this work, we explore some domain-independent qualitative and quantitative features in raw trajectory (spatio-temporal) data in videos captured by a fixed single wide-angle view camera sensor in outdoor areas. We study the efficacy of those features in classifying four basic high level actions by employing two supervised learning algorithms and show how each of the features affect the learning algorithms’ overall accuracy as a single factor or confounded with others.

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vii, 27 pages : illustrations (some color)

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  • May 2015

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  • Feb. 9, 2016, 4:37 p.m.

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  • Feb. 16, 2017, 6:16 a.m.

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Janmohammadi, Siamak. Classifying Pairwise Object Interactions: A Trajectory Analytics Approach, thesis, May 2015; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc801901/: accessed May 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .