Motion Saliency Detection for Surveillance Systems Using Streaming Dynamic Mode Decomposition

Clicks: 335
ID: 111068
2020
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Abstract
Intelligent surveillance systems enable secured visibility features in the smart city era. One of the major models for pre-processing in intelligent surveillance systems is known as saliency detection, which provides facilities for multiple tasks such as object detection, object segmentation, video coding, image re-targeting, image-quality assessment, and image compression. Traditional models focus on improving detection accuracy at the cost of high complexity. However, these models are computationally expensive for real-world systems. To cope with this issue, we propose a fast-motion saliency method for surveillance systems under various background conditions. Our method is derived from streaming dynamic mode decomposition (s-DMD), which is a powerful tool in data science. First, DMD computes a set of modes in a streaming manner to derive spatial–temporal features, and a raw saliency map is generated from the sparse reconstruction process. Second, the final saliency map is refined using a difference-of-Gaussians filter in the frequency domain. The effectiveness of the proposed method is validated on a standard benchmark dataset. The experimental results show that the proposed method achieves competitive accuracy with lower complexity than state-of-the-art methods, which satisfies requirements in real-time applications.
Reference Key
huh2020symmetrymotion Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Thien-Thu Ngo,Vandung Nguyen,Xuan-Qui Pham,Md-Alamgir Hossain,Eui-Nam Huh;Thien-Thu Ngo;Vandung Nguyen;Xuan-Qui Pham;Md-Alamgir Hossain;Eui-Nam Huh;
Journal Symmetry
Year 2020
DOI
10.3390/sym12091397
URL
https://sciprofiles.com/publication/view/2189eff71476beadbebe5ccd7ad7d147
Keywords
surveillance systems dynamic mode decomposition motion saliency surveillance systems dynamic mode decomposition motion saliency

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