標題: Robust background subtraction with shadow and highlight removal for indoor surveillance
作者: Hu, Jwu-Sheng
Su, Tzung-Min
Jeng, Shr-Chi
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: background subtraction;Gaussian mixture model;shadow removal;surveillance
公開日期: 2006
摘要: This work describes a new 3D cone-shape illumination model (CSIM) and a robust background subtraction scheme involving shadow and highlight removal for indoor-environmental surveillance. Foreground objects can be precisely extracted for various post-processing procedures such as recognition. Gaussian mixture model (GMM) is applied to construct a color-based probabilistic background model (CBM) that contains the short-term color-based background model (STCBM) and the long-term color-based background model (LTCBM). STCBM and LTCBM are then proposed to build the gradient-based version of the probabilistic background model (GBM) and the CSIM. In the CSIM, a new dynamic cone-shape boundary in the RGB color space is proposed to distinguish pixels among shadow, highlight and foreground. Furthermore, CBM can be used to determine the threshold values of CSIM. A novel scheme combining the CBM, GBM and CSIM is proposed to determine the background. The effectiveness of the proposed method is demonstrated via experiments in a complex indoor environment.
URI: http://hdl.handle.net/11536/17495
http://dx.doi.org/10.1109/IROS.2006.282156
ISBN: 978-1-4244-0258-8
DOI: 10.1109/IROS.2006.282156
期刊: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12
起始頁: 4545
結束頁: 4550
Appears in Collections:Conferences Paper


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