Motion segmentation based on dual interrelated models

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• 作者：Zhihui Fan ; Zheqing Li ; Peiyu Li ; Hui Wang
• 关键词：motion segmentation ; object detection ; shadow removal ; background subtraction ; surveillance
• 刊名：Wuhan University Journal of Natural Sciences
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：22
• 期：1
• 页码：79-84
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Life Sciences, general; Materials Science, general; Computer Science, general;
• 出版者：Wuhan University
• ISSN：1993-4998
• 卷排序：22

文摘

Motion segmentation plays an important role in many vision applications, yet it is still a challenging problem in complex scenes. The typical conditions in real world scenarios like illumination variations, dynamic backgrounds and camera shaking make negative effects on segmentation performance. In this paper, a newly designed method for robust motion segmentation is proposed, which is mainly composed of two interrelated models. One is a normal random model(N-model), and the other is called enhanced random model(E-model). They are constructed and updated in spatio-temporal information for adapting to illumination changes and dynamic backgrounds, and operate in an Ada- Boost-like strategy. The exhaustive experimental evaluations on complex scenes demonstrate that the proposed method outperforms the state-of-the-art methods.