Unsupervised Moving Object Detection via Contextual Information Separation

Yanchao Yang, Antonio Loquercio, Davide Scaramuzza, Stefano Soatto; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 879-888

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.

Related Material


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[bibtex]
@InProceedings{Yang_2019_CVPR,
author = {Yang, Yanchao and Loquercio, Antonio and Scaramuzza, Davide and Soatto, Stefano},
title = {Unsupervised Moving Object Detection via Contextual Information Separation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}