Object class detection: A survey

  • Authors:

  • Xin Zhang;Yee-Hong Yang;Zhiguang Han;Hui Wang;Chao Gao

  • Affiliations:

  • National University of Defense Technology, China;University of Alberta, Canada;National University of Defense Technology, China;National University of Defense Technology, China;National University of Defense Technology, China

  • Venue:
  • ACM Computing Surveys (CSUR)
  • Year:
  • 2013

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Abstract

Object class detection, also known as category-level object detection, has become one of the most focused areas in computer vision in the new century. This article attempts to provide a comprehensive survey of the recent technical achievements in this area of research. More than 270 major publications are included in this survey covering different aspects of the research, which include: (i) problem description: key tasks and challenges; (ii) core techniques: appearance modeling, localization strategies, and supervised classification methods; (iii) evaluation issues: approaches, metrics, standard datasets, and state-of-the-art results; and (iv) new development: particularly new approaches and applications motivated by the recent boom of social images. Finally, in retrospect of what has been achieved so far, the survey also discusses what the future may hold for object class detection research.