DocumentCode :
177253
Title :
Object localization and tracking based on multiple sensor fusion in intelligent home
Author :
Jianqin Yin ; Guohui Tian ; Guodong Li
Author_Institution :
Sch. of Inf. Sci. & Eng., Univ. of Jinan, Jinan, China
fYear :
2014
fDate :
May 31 2014-June 2 2014
Firstpage :
5266
Lastpage :
5270
Abstract :
A novel scheme for object localization and tracking under family environment is presented based on fusion of multiple sensors, which include two laser sensors and camera sensors. The two laser sensors and two cameras are used to locate the object separately, and multiple sensors probability data association fusion algorithm is used to track the objects. Firstly, object detection is realized by laser sensors and vision sensors separately. Secondly, the laser data is fused by Extended Kalman Filter. To obtain the vision location results, background model is built by adaptive background updating based on motion history images. Background subtraction is used to acquire the original location result, which is filtered by Kalman Filter. Finally, multiple sensors probability data association fusion algorithm is used to fuse the different kinds of data. Experimental results show that the scheme can efficiently solve the problem of object localization and tracking.
Keywords :
Kalman filters; cameras; computer vision; home computing; image fusion; image motion analysis; object tracking; probability; adaptive background updating; background subtraction; camera sensors; extended Kalman filter; family environment; intelligent home; laser sensors; motion history images; multiple sensor fusion; object detection; object localization; object tracking; probability data association fusion algorithm; vision location; Cameras; Covariance matrices; Educational institutions; Laser fusion; Laser modes; Robot sensing systems; Intelligent Space; Multiple Sensors Fusion; Object Localization and Tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
Type :
conf
DOI :
10.1109/CCDC.2014.6853120
Filename :
6853120
Link To Document :
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