Unsupervised Moving Object Detection via Contextual Information Separation

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 possib...

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Bibliographic Details
Published inProceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 879 - 888
Main Authors Yang, Yanchao, Loquercio, Antonio, Scaramuzza, Davide, Soatto, Stefano
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2019
Subjects
Computer vision
Deep learning
Grouping and Shape
Mathematical models
Neural networks
Object detection
Partial differential equations
Pattern recognition
Representation Learning
Scene Analysis and Understanding
Segmentation
Statistical Learning
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Summary: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.
ISSN:1063-6919
DOI:10.1109/CVPR.2019.00097