DocumentCode :
2372266
Title :
Real-time object tracking via CamShift-based robust framework
Author :
Xin Chen ; Xiang Li ; Hefeng Wu ; Taisheng Qiu
Author_Institution :
State-Province Joint Lab. of Digital Home Interactive Applic., Sun Yat-sen Univ., Guangzhou, China
fYear :
2012
fDate :
23-25 March 2012
Firstpage :
527
Lastpage :
530
Abstract :
In recent years, lots of object tracking methods have been presented for better tracking accuracies. However, few of them can be applied to the real-time applications due to high computational cost. Aiming at achieving better realtime tracking performance, we propose an adaptive robust framework for object tracking based on the CamShift approach, which is notable for its simplicity and high processing efficiency. An adaptive local search method is presented to search for the best object candidate to avoid that the CamShift tracker gets confused by the surrounding background and erroneously incorporates it into the object region. A Kalman filter is also incorporated into our framework for prediction of the object´s movement, so as to reduce the search effort and possible tracking failure caused by fast object motion. The experimental results demonstrate that the proposed tracking framework is robust and computationally effective.
Keywords :
Kalman filters; object tracking; real-time systems; search problems; CamShift-based robust framework; Kalman filter; adaptive local search; adaptive robust framework; real-time applications; real-time object tracking; real-time tracking performance; Conferences; Kalman filters; Real time systems; Robustness; Search problems; Target tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Technology (ICIST), 2012 International Conference on
Conference_Location :
Hubei
Print_ISBN :
978-1-4577-0343-0
Type :
conf
DOI :
10.1109/ICIST.2012.6221702
Filename :
6221702
Link To Document :
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