Object class detection

Author:

Zhang Xin1,Yang Yee-Hong2,Han Zhiguang1,Wang Hui1,Gao Chao1

Affiliation:

1. National University of Defense Technology, China

2. University of Alberta, Canada

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.

Funder

China Scholarship Council

Natural Sciences and Engineering Research Council of Canada

National Natural Science Foundation of China

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

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