Volumetric Features for Video Event Detection |
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Authors: | Yan Ke Rahul Sukthankar Martial Hebert |
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Affiliation: | 1.School of Computer Science,Carnegie Mellon,Pittsburgh,USA;2.Intel Labs Pittsburgh,Pittsburgh,USA |
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Abstract: | 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 the
volumetric representation of an event against oversegmented spatio-temporal video volumes; (2) we augment our shape-based
features using flow; (3) rather than treating an event template as an atomic entity, we separately match by parts (both in
space and time), enabling robustness against occlusions and actor variability. Our experiments on human actions, such as picking
up a dropped object or waving in a crowd show reliable detection with few false positives. |
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Keywords: | |
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