AMBER: Adapting multi-resolution background extractor
Bin Wang, Piotr Dudek · 2013
In this paper, a fast self-adapting multi-resolution background detection algorithm is introduced. A pixel-based background model is proposed, that represents not only each pixel's background values, but also their efficacies, so that new background values always replace the least effective ones. Model maintenance and global control processes ensure fast initialization, adaptation to background changes with different timescales, restrain the generation of ghosts, and adjust the decision thresholds based on noise levels. Evaluation results indicate that the proposed algorithm outperforms most other state-of-the-art algorithms not only in terms of accuracy, but also in terms of processing speed and memory requirements.