English  |  正體中文  |  简体中文  |  Items with full text/Total items : 58317/91854 (63%)
Visitors : 14029644      Online Users : 112
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118151

    Title: Real-Time Multi-Scale Parallel Compressive Tracking
    Authors: Tsai, Chi-Yi;Feng, Yen-Chang
    Keywords: Robust visual tracking;Compressive tracking;Multi-scale classification;Parallel processing;Algorithmic acceleration
    Date: 2017-08-31
    Issue Date: 2020-03-03 12:10:18 (UTC+8)
    Publisher: Springer Berlin Heidelberg
    Abstract: 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.
    Relation: Journal of Real-Time Image Processing 16(6), p.2073-2091
    DOI: 10.1007/s11554-017-0713-4
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

    Files in This Item:

    File Description SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback