Abstract / Description of output
We present a technique for separating foreground objects from the background in a video. Our method is fast, fully automatic, and makes minimal assumptions about the video. This enables handling essentially unconstrained settings, including rapidly moving background, arbitrary object motion and appearance, and non-rigid deformations and articulations. In experiments on two datasets containing over 1400 video shots, our method outperforms a state-of-the-art background subtraction technique [4] as well as methods based on clustering point tracks [6, 18, 19]. Moreover, it performs comparably to recent video object segmentation methods based on object proposals [14, 16, 27], while being orders of magnitude faster.
Original language | English |
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Title of host publication | Computer Vision (ICCV), 2013 IEEE International Conference on |
Pages | 1777-1784 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 1 Dec 2013 |
Keywords / Materials (for Non-textual outputs)
- image motion analysis
- image segmentation
- object recognition
- video signal processing
- background subtraction technique
- clustering point
- fast object segmentation
- foreground object separation
- nonrigid deformation
- object motion analysis
- unconstrained video
- video object segmentation
- video shot
- Adaptive optics
- Estimation
- Labeling
- Motion segmentation
- Object segmentation
- Optical imaging
- Optical variables measurement
- video
- video segmentation