Instance-level video segmentation from object tracks

Guillaume Seguin 1, 2 Piotr Bojanowski 1, 2 Rémi Lajugie 1, 3 Ivan Laptev 2
2 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, Inria de Paris
3 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : We address the problem of segmenting multiple object instances in complex videos. Our method does not require manual pixel-level annotation for training, and relies instead on readily-available object detectors or visual object tracking only. Given object bounding boxes at input, we cast video segmentation as a weakly-supervised learning problem. Our proposed objective combines (a) a discrim-inative clustering term for background segmentation, (b) a spectral clustering one for grouping pixels of same object instances, and (c) linear constraints enabling instance-level segmentation. We propose a convex relaxation of this problem and solve it efficiently using the Frank-Wolfe algorithm. We report results and compare our method to several base-lines on a new video dataset for multi-instance person seg-mentation.
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Guillaume Seguin, Piotr Bojanowski, Rémi Lajugie, Ivan Laptev. Instance-level video segmentation from object tracks. CVPR 2016, IEEE, Jun 2016, Las Vegas, United States. ⟨hal-01255765⟩

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