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arXiv · 1901.03360

Unsupervised Moving Object Detection via Contextual Information Separation

Yanchao Yang·Antonio Loquercio·Davide Scaramuzza·Stefano Soatto
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Abstract

We propose an adversarial contextual model for detecting moving objects in images. A deep neural network is trained to predict the optical flow in a region using information from everywhere else but that region (context), while another network attempts to make such context as uninformative as possible. The result is a model where hypotheses naturally compete with no need for explicit regularization or hyper-parameter tuning. Although our method requires no supervision whatsoever, it outperforms several methods that are pre-trained on large annotated datasets. Our model can be thought of as a generalization of classical variational generative region-based segmentation, but in a way that avoids explicit regularization or solution of partial differential equations at run-time.

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Yanchao Yang, Antonio Loquercio, Davide Scaramuzza, Stefano Soatto. 2019-01-10. Unsupervised Moving Object Detection via Contextual Information Separation. https://arxiv.org/abs/1901.03360

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