Particle Video: Long-Range Motion Estimation Using Point Trajectories |
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Authors: | Peter Sand Seth Teller |
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Affiliation: | (1) MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA |
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Abstract: | This paper describes a new approach to motion estimation in video. We represent video motion using a set of particles. Each
particle is an image point sample with a long-duration trajectory and other properties. To optimize particle trajectories
we measure appearance consistency along the particle trajectories and distortion between the particles. The resulting motion
representation is useful for a variety of applications and cannot be directly obtained using existing methods such as optical
flow or feature tracking. We demonstrate the algorithm on challenging real-world videos that include complex scene geometry,
multiple types of occlusion, regions with low texture, and non-rigid deformations. |
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Keywords: | Video motion estimation Optical flow Feature tracking |
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