Fast Object Detection with Occlusions

  • Lin Y
  • Liu T
  • Fuh C
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We describe a new framework, based on boosting algorithms and cascade structures, to efficiently detect objects/faces with occlusions. While our approach is motivated by the work of Viola and Jones, several techniques have been developed for establishing a more general system, including (i) a robust boosting scheme, to select useful weak learners and to avoid overfitting; (ii) reinforcement training, to reduce false-positive rates via a more effective training procedure for boosted cascades; and (iii) cascading with evidence, to extend the system to handle occlusions, without compromising in detection speed. Experimental results on de- tecting faces under various situations are provided to demonstrate the performances of the proposed method.

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  • SGR: 35048863370
  • SCOPUS: 2-s2.0-35048863370
  • PUI: 39752014
  • ISSN: 03029743


  • Yen-Yu Lin

  • Tyng-Luh Liu

  • Chiou-Shann Fuh

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