Background Subtraction Network Module Ensemble for Background Scene Adaptation | IEEE Conference Publication | IEEE Xplore

Background Subtraction Network Module Ensemble for Background Scene Adaptation


Abstract:

Background subtraction networks outperform traditional hand-craft background subtraction methods. The main advantage of background subtraction networks is their ability t...Show More

Abstract:

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.
Date of Conference: 29 November 2022 - 02 December 2022
Date Added to IEEE Xplore: 24 November 2022
ISBN Information:
Conference Location: Madrid, Spain

References

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