TY - GEN
T1 - Motion detectio n based on background modeling and performance analysis for outdoor surveillance
AU - Huang, Tianci
AU - Qiu, Jingbang
AU - Sakayori, Takahiro
AU - Goto, Satoshi
AU - Ikenaga, Takeshi
PY - 2009
Y1 - 2009
N2 - Real-time segmentation of moving objects in video sequences is a fundamental step for surveillance systems. One of successful methods for complex background is to use a multi-color background model per pixel. However, Common problem for this approach is that it suffers from illumination changing environment, in addition, it is incapable of removing shadows of moving objects. This paper proposed an effective scheme to improve the adaptive background model for each pixel by introducing a background training parameter into every Gaussian model, and region-based scheme is applied to judgment by utilizing both spatial and temporal information. Experimental results will be presented to validate proposed algorithm keep robustness in the situation of illumination changes, shadow can be removed in foreground mask, results shows False Alarm Rate can be reduced from 34.9% to 35.8% while the overlap varies within normal range from 0.4 to 0.6 compared with conventional Gaussian mixture model.
AB - Real-time segmentation of moving objects in video sequences is a fundamental step for surveillance systems. One of successful methods for complex background is to use a multi-color background model per pixel. However, Common problem for this approach is that it suffers from illumination changing environment, in addition, it is incapable of removing shadows of moving objects. This paper proposed an effective scheme to improve the adaptive background model for each pixel by introducing a background training parameter into every Gaussian model, and region-based scheme is applied to judgment by utilizing both spatial and temporal information. Experimental results will be presented to validate proposed algorithm keep robustness in the situation of illumination changes, shadow can be removed in foreground mask, results shows False Alarm Rate can be reduced from 34.9% to 35.8% while the overlap varies within normal range from 0.4 to 0.6 compared with conventional Gaussian mixture model.
KW - Background
KW - False Alarm Rate
KW - Gaussian mixture model (GMM)
KW - Training
UR - http://www.scopus.com/inward/record.url?scp=64849092663&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=64849092663&partnerID=8YFLogxK
U2 - 10.1109/ICCMS.2009.15
DO - 10.1109/ICCMS.2009.15
M3 - Conference contribution
AN - SCOPUS:64849092663
SN - 9780769535623
T3 - Proceedings - 2009 International Conference on Computer Modeling and Simulation, ICCMS 2009
SP - 38
EP - 42
BT - Proceedings - 2009 International Conference on Computer Modeling and Simulation, ICCMS 2009
T2 - 2009 International Conference on Computer Modeling and Simulation, ICCMS 2009
Y2 - 20 February 2009 through 22 February 2009
ER -