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Pedestrian detection / Video tracking / Artificial intelligence / Optical flow / Activity recognition


Learning an image-based motion context for multiple people tracking Laura Leal-Taix´e1,2 , Michele Fenzi2 , Alina Kuznetsova2 , Bodo Rosenhahn2 , and Silvio Savarese3 1 2
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Document Date: 2014-05-10 00:44:20


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File Size: 3,67 MB

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City

Milan / Mcmc / /

Company

2D Pedcross2 LP / 2D Crossing LP / Jelmoli LP / 2D LP / Bahnhof LP / Sunnyday LP / Proposed LP / /

Event

Product Recall / Product Issues / /

Facility

ETH Z¨urich Institute / Leibniz University / /

IndustryTerm

greedy algorithms / autonomous car navigation / online learned crf model / post-processing / /

NaturalFeature

Hough forests / Hough Random Forest / Random Forests / Random Forest / Hough Random Forests / /

Organization

Computational Vision and Geometry Lab / Stanford University / ETH Z¨urich Institute for Information Processing / Leibniz University Hannover / /

Person

Laura Leal-Taix / S. Khamis / V / /

Position

generalized model for capturing interactions among individuals / learned model for multiple people tracking / Social force model for pedestrian dynamics / online learned crf model for multi-target tracking / flow model for joint action recognition and identity maintenance / /

Product

rates / rate / Hannover / /

ProvinceOrState

New Brunswick / /

PublishedMedium

Machine Learning / /

Technology

Remote Sensing / proposed algorithm / Machine Learning / simulation / animation / tracking algorithm / /

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