Weakly Supervised Realtime Dynamic Background Subtraction
- URL: http://arxiv.org/abs/2303.02857v1
- Date: Mon, 6 Mar 2023 03:17:48 GMT
- Title: Weakly Supervised Realtime Dynamic Background Subtraction
- Authors: Fateme Bahri and Nilanjan Ray
- Abstract summary: We propose a weakly supervised framework that can perform background subtraction without requiring per-pixel ground-truth labels.
Our framework is trained on a moving object-free sequence of images and comprises two networks.
Our proposed method is online, real-time, efficient, and requires minimal frame-level annotation.
- Score: 8.75682288556859
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Background subtraction is a fundamental task in computer vision with numerous
real-world applications, ranging from object tracking to video surveillance.
Dynamic backgrounds poses a significant challenge here. Supervised deep
learning-based techniques are currently considered state-of-the-art for this
task. However, these methods require pixel-wise ground-truth labels, which can
be time-consuming and expensive. In this work, we propose a weakly supervised
framework that can perform background subtraction without requiring per-pixel
ground-truth labels. Our framework is trained on a moving object-free sequence
of images and comprises two networks. The first network is an autoencoder that
generates background images and prepares dynamic background images for training
the second network. The dynamic background images are obtained by thresholding
the background-subtracted images. The second network is a U-Net that uses the
same object-free video for training and the dynamic background images as
pixel-wise ground-truth labels. During the test phase, the input images are
processed by the autoencoder and U-Net, which generate background and dynamic
background images, respectively. The dynamic background image helps remove
dynamic motion from the background-subtracted image, enabling us to obtain a
foreground image that is free of dynamic artifacts. To demonstrate the
effectiveness of our method, we conducted experiments on selected categories of
the CDnet 2014 dataset and the I2R dataset. Our method outperformed all
top-ranked unsupervised methods. We also achieved better results than one of
the two existing weakly supervised methods, and our performance was similar to
the other. Our proposed method is online, real-time, efficient, and requires
minimal frame-level annotation, making it suitable for a wide range of
real-world applications.
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