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