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Real-world actions occur often in crowded, dynamic environments. This poses a difficult challenge for current approaches to video event detection because it is difficult to segment the actor from the background due to distracting motion from other objects in the scene. We propose a technique for event recognition in crowded videos that reliably identifies actions in the presence of partial occlusion and background clutter. Our approach is based on three key ideas: (1) we efficiently match thedoi:10.1109/iccv.2007.4409011 dblp:conf/iccv/KeSH07 fatcat:4zbyaybqwjc2lgx2xelbtllihe