DocumentCode :
3660099
Title :
A robust mean-transform based visual tracker
Author :
Hing Tuen Yau;Zhe Zhang;Ho Chuen Kam;Kin Hong Wong
Author_Institution :
Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, N.T., Hong Kong
fYear :
2015
Firstpage :
493
Lastpage :
498
Abstract :
We present a robust particle filter based visual tracker based on an earlier approach called mean-transform which can track a window with orientation and scale changes. This work is the first work combining sparse coding, mean transform and particle filtering in visual tracking. We show that particle filter is effective in enhancing the mean-transform tracker. From the result, we see that such architecture can provide comparable accuracy to the state-of-art trackers with increased robustness. The current approach may provide a framework for investigating a state approach that incorporates velocity and acceleration of objects in the tracker.
Keywords :
"Visualization","Robustness","Tracking","Transforms","Histograms","Conferences","Computer vision"
Publisher :
ieee
Conference_Titel :
Information and Automation, 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/ICInfA.2015.7279338
Filename :
7279338
Link To Document :
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