期刊論文
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 |
相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118151 ) |