Background Subtraction Network Module Ensemble for Background Scene Adaptation

Background subtraction networks outperform traditional hand-craft background subtraction methods. The main advantage of background subtraction networks is their ability to automatically learn background features for training scenes. When applying the trained network to new target scenes, adapting th...

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Bibliographic Details
Published in:	2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) pp. 1 - 8
Main Authors:	Hamada, Taiki, Minematsu, Tsubasa, Simada, Atsushi, Okubo, Fumiya, Taniguchi, Yuta
Format:	Conference Proceeding
Language:	English
Published:	IEEE 29-11-2022
Subjects:	Adaptation models Feature extraction Focusing Knowledge engineering Lighting Surveillance Training
Online Access:	Get full text
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Description
Summary:	Background subtraction networks outperform traditional hand-craft background subtraction methods. The main advantage of background subtraction networks is their ability to automatically learn background features for training scenes. When applying the trained network to new target scenes, adapting the network to the new scenes is crucial. However, few studies have focused on reusing multiple trained models for new target scenes. Considering background changes have several categories, such as illumination changes, a model trained for each background scene can work effectively for the target scene similar to the training scene. In this study, we propose a method to ensemble the module networks trained for each background scene. Experimental results show that the proposed method is significantly more accurate compared with the conventional methods in the target scene by tuning with only a few frames.
DOI:	10.1109/AVSS56176.2022.9959316