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Unsupervised Object Discovery and Tracking in Video Collections
lib:a65945e4ed0a4200 (v1.0.0)
Authors: Suha Kwak,Minsu Cho,Ivan Laptev,Jean Ponce,Cordelia Schmid
Where published: ICCV 2015 12
ArXiv: 1505.03825
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1505.03825v1
This paper addresses the problem of automatically localizing dominant objects as spatio-temporal tubes in a noisy collection of videos with minimal or even no supervision. We formulate the problem as a combination of two complementary processes: discovery and tracking. The first one establishes correspondences between prominent regions across videos, and the second one associates successive similar object regions within the same video. Interestingly, our algorithm also discovers the implicit topology of frames associated with instances of the same object class across different videos, a role normally left to supervisory information in the form of class labels in conventional image and video understanding methods. Indeed, as demonstrated by our experiments, our method can handle video collections featuring multiple object classes, and substantially outperforms the state of the art in colocalization, even though it tackles a broader problem with much less supervision.
Where published: ICCV 2015 12
ArXiv: 1505.03825
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1505.03825v1
This paper addresses the problem of automatically localizing dominant objects as spatio-temporal tubes in a noisy collection of videos with minimal or even no supervision. We formulate the problem as a combination of two complementary processes: discovery and tracking. The first one establishes correspondences between prominent regions across videos, and the second one associates successive similar object regions within the same video. Interestingly, our algorithm also discovers the implicit topology of frames associated with instances of the same object class across different videos, a role normally left to supervisory information in the form of class labels in conventional image and video understanding methods. Indeed, as demonstrated by our experiments, our method can handle video collections featuring multiple object classes, and substantially outperforms the state of the art in colocalization, even though it tackles a broader problem with much less supervision.
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