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

替代計量分析
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其它資訊
摘要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.
著者Yau H.T., Zhang Z., Kam H.C., Wong K.H.
會議名稱2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics
會議開始日08.08.2015
會議完結日10.08.2015
會議地點Yunnan
會議國家/地區中國
出版年份2015
月份9
日期28
頁次493 - 498
國際標準書號9781467391047
語言英式英語
關鍵詞mean transform, particle filter, sparse coding, visual tracking

上次更新時間 2020-07-07 於 00:58