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Particle video: Long-range motion estimation using point trajectories
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AI
DOI：10.1007/s11263-008-0136-6.png)
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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.
Keyword:
video motion estimation
optical flow
feature tracking
期刊
IF:
9.3
论文数:
3.9K
被引数:
2.8W
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