Efficient background model based on multi-level feedback for video surveillance

Song Tang; Bingshu Wang; Yong Zhao; Xuefeng Hu; Yuanzhi Gong

doi:10.1117/12.2244495

29 August 2016 Efficient background model based on multi-level feedback for video surveillance

Song Tang, Bingshu Wang, Yong Zhao, Xuefeng Hu, Yuanzhi Gong

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100335L (2016) https://doi.org/10.1117/12.2244495
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

Segmentation of moving objects from video sequences is the fundamental step in intelligent surveillance applications. Numerous methods have been proposed to obtain object segmentation. In this paper, we present an effective approach based on the mixture of Gaussians. The approach makes use of a feedback strategy with multiple levels: the pixel level, the region level, and the frame level. Pixel-level feedback helps to provide each pixel with an adaptive learning rate. The maintenance strategy of the background model is adjusted by region-level feedback based on tracking. Frame-level feedback is used to detect the global change in scenes. These different levels of feedback strategies ensure our approach’s effectiveness and robustness. This is demonstrated through experimental results on the Change Detection 2014 benchmark dataset.

Citation Download Citation

Song Tang, Bingshu Wang, Yong Zhao, Xuefeng Hu, and Yuanzhi Gong "Efficient background model based on multi-level feedback for video surveillance", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100335L (29 August 2016); https://doi.org/10.1117/12.2244495

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