淡江大學機構典藏:Item 987654321/118151
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    题名: Real-Time Multi-Scale Parallel Compressive Tracking
    作者: Tsai, Chi-Yi;Feng, Yen-Chang
    关键词: Robust visual tracking;Compressive tracking;Multi-scale classification;Parallel processing;Algorithmic acceleration
    日期: 2017-08-31
    上传时间: 2020-03-03 12:10:18 (UTC+8)
    出版者: Springer Berlin Heidelberg
    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.
    關聯: Journal of Real-Time Image Processing 16(6), p.2073-2091
    DOI: 10.1007/s11554-017-0713-4
    显示于类别:[電機工程學系暨研究所] 期刊論文

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