Earticle

초록

영어

Locating persons in crowded scenes is very difficult due to multi-resolution and complex environment. The other difficulty in pedestrian detection domain is the real time requirement, because the camera installed on the crossing road is in high definition. In this paper, we presented a multi-task pedestrian detection framework boosted by Bing feature. We firstly trained upright full-body, multi-person, half-body and head models, then we compute the object-ness score and generate 1000 proposals by Bing feature, and at last we apply different model to different aspect ratio of the detection proposals. The experiment results on the PASCAL VOC 2007 show that our method outperforms all the other methods and achieved lower miss rate than the state-of-the-art. The computation time cost is just the half of state-of-the-art method.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1. Objectiveness Measure
  2.2. Pedestrian Detection
 3. Methodology
  3.1. Objectiveness Measure by Bing Feature
  3.2. Multi-task Pedestrian Detection
 4. Experiments and Comparison
  4.1. Weighting Scheme and Spatial Predicate
  4.2. Experiment Setup
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Shilin Zhang [ North China University of Technology ]
• Xunyuan Zhang [ North China University of Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605