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

Cited by 72 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. マスク着用に対応した顔検出手法;The Journal of The Institute of Image Information and Television Engineers;2024

2. Scale Variant Vehicle Object Recognition by CNN Module of Multi-Pooling-PCA Process;Journal of Intelligent and Connected Vehicles;2023-12

3. IOP-CapsNet with ISEMRA: Fetching part-to-whole topology for improving detection performance of articulated instances;Expert Systems with Applications;2023-09

4. DR-CapsNet with CAEMRA: Looking deep inside instance for boosting object detection effect;Engineering Applications of Artificial Intelligence;2023-08

5. Intelligent Tools and Whole-process Control System Based on Artificial Intelligence and Internet of Things Technology;2023 Panda Forum on Power and Energy (PandaFPE);2023-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3