Real-Time Multi-Scale Parallel Compressive Tracking
學年 108
學期 1
出版（發表）日期 2019-12-01
作品名稱 Real-Time Multi-Scale Parallel Compressive Tracking
作品名稱（其他語言）
著者 Chi-Yi Tsai; Yen-Chang Feng
單位
出版者
著錄名稱、卷期、頁數 Journal of Real-Time Image Processing 16(6), p.2073-2091
摘要 Robust visual tracking is a challenging problem because the appearance of a target may rapidly change due to significant variations in the object’s motion and the surrounding illumination. In this paper, a novel robust visual tracking algorithm is proposed based on an existing compressive tracking method. The proposed algorithm adopts multiple naive Bayes classifiers, each trained under a different scale condition, to realize online parallel multi-scale classification. Further, each classifier was initialized by randomly generating different types of Haar-like features. By doing so, the robustness of the feature classification can be improved to obtain more accurate tracking results. To enhance the real-time performance of the visual tracking system, the formula of the naive Bayes classifier is studied and simplified to speed up the processing speed of parallel multi-scale feature classification. After acceleration via formula simplification and parallel implementation, the proposed visual tracking algorithm can reach a tracking performance of approximately 45 frames per second (fps) when dealing with images of 642 × 352 pixels on a popular Intel Core i5-3230M platform. The experimental results show that the proposed algorithm outperforms state-of-the-art visual tracking methods on challenging videos in terms of success rate, tracking accuracy, and visual comparison.
關鍵字 Robust visual tracking;Compressive tracking;Multi-scale classification;Parallel processing;Algorithmic acceleration
語言 en
ISSN 1861-8200
期刊性質 國外
收錄於 SCI
產學合作 國內
通訊作者
審稿制度 是
國別 DEU
公開徵稿
出版型式 ,電子版,紙本
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118151 )

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