A fully-convolutional neural network for background subtraction of unseen videos

Date Issued
2019Author(s)
Tezcan, Mustafa Ozan
Konrad, Janusz
Ishwar, Prakash
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https://hdl.handle.net/2144/40673Version
Accepted manuscript
Citation (published version)
Mustafa Ozan Tezcan, Janusz Konrad, Prakash Ishwar. 2019. "A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos." CoRR, Volume abs/1907.11371. https://arxiv.org/abs/1907.11371.Abstract
Background subtraction is a basic task in computer vision
and video processing often applied as a pre-processing step
for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have
been proposed, however nearly all of the top-performing
ones are supervised. Crucially, their success relies upon
the availability of some annotated frames of the test video
during training. Consequently, their performance on completely “unseen” videos is undocumented in the literature.
In this work, we propose a new, supervised, backgroundsubtraction algorithm for unseen videos (BSUV-Net) based
on a fully-convolutional neural network. The input to our
network consists of the current frame and two background
frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance
of overfitting, we also introduce a new data-augmentation
technique which mitigates the impact of illumination difference between the background frames and the current frame.
On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of
several metrics including F-measure, recall and precision.
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