A robust mean-transform based visual tracker
Refereed conference paper presented and published in conference proceedings


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AbstractWe 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.
All Author(s) ListYau H.T., Zhang Z., Kam H.C., Wong K.H.
Name of Conference2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics
Start Date of Conference08/08/2015
End Date of Conference10/08/2015
Place of ConferenceYunnan
Country/Region of ConferenceChina
Year2015
Month9
Day28
Pages493 - 498
ISBN9781467391047
LanguagesEnglish-United Kingdom
Keywordsmean transform, particle filter, sparse coding, visual tracking

Last updated on 2020-15-08 at 23:12