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  • 标题:Fast and Accurate Object Detection Based on Binary Co-occurrence Features
  • 本地全文:下载
  • 作者:Mitsuru Ambai ; Taketo Kimura ; Chiori Sakai
  • 期刊名称:Information and Media Technologies
  • 电子版ISSN:1881-0896
  • 出版年度:2015
  • 卷号:10
  • 期号:3
  • 页码:464-467
  • DOI:10.11185/imt.10.464
  • 出版社:Information and Media Technologies Editorial Board
  • 摘要:In this paper, we propose a fast and accurate object detection algorithm based on binary co-occurrence features. In our method, co-occurrences of all the possible pairs of binary elements in a block of binarized HOG are enumerated by logical operations, i.g. circular shift and XOR. This resulted in extremely fast co-occurrence extraction. Our experiments revealed that our method can process a VGA-size image at 64.6fps, that is two times faster than the camera frame rate (30fps), on only a single core of CPU (Intel Core i7-3820 3.60GHz), while at the same time achieving a higher classification accuracy than original (real-valued) HOG in the case of a pedestrian detection task.
  • 关键词:binary features;object detection;co-occurrence
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