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  • 标题:Occlusion-aware Visual Tracker using Spatial Structural Information and Dominant Features
  • 本地全文:下载
  • 作者:Rongtai Cai ; Peng Zhu
  • 期刊名称:The International Arab Journal of Information Technology
  • 印刷版ISSN:1683-3198
  • 出版年度:2021
  • 卷号:18
  • 期号:2
  • DOI:10.34028/iajit/18/2/7
  • 语种:English
  • 出版社:Zarqa Private University
  • 摘要:To overcome the problem of occlusion in visual tracking, this paper proposes an occlusion-aware tracking algorithm. The proposed algorithm divides the object into discrete image patches according to the pixel distribution of the object by means of clustering. To avoid the drifting of the tracker to false targets, the proposed algorithm extracts the dominant features, such as color histogram or histogram of oriented gradient orientation, from these image patches, and uses them as cues for tracking. To enhance the robustness of the tracker, the proposed algorithm employs an implicit spatial structure between these patches as another cue for tracking; Afterwards, the proposed algorithm incorporates these components into the particle filter framework, which results in a robust and precise tracker. Experimental results on color image sequences with different resolutions show that the proposed tracker outperforms the comparison algorithms on handling occlusion in visual tracking.
  • 关键词:Visual tracking;feature fusion;occlusion-aware tracking;particle filter;part-based tracking
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