ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (12): 2724-2747.doi: 10.7544/issn1000-1239.2021.20200699

• 人工智能 • 上一篇    下一篇

基于深度学习的人群计数研究综述

余鹰1,朱慧琳1,钱进1,潘诚1,苗夺谦1,2   

  1. 1(华东交通大学软件学院 南昌 330013);2(同济大学电子与信息工程学院 上海 201804) (yuyingjx@163.com)
  • 出版日期: 2021-12-01
  • 基金资助: 
    国家自然科学基金项目(62163016,62066014);江西省自然科学基金项目(20212ACB202001,20202BABL202018)

Survey on Deep Learning Based Crowd Counting

Yu Ying1, Zhu Huilin1, Qian Jin1, Pan Cheng1, Miao Duoqian1,2   

  1. 1(School of Software, East China Jiaotong University, Nanchang 330013);2(College of Electronics and Information Engineering, Tongji University, Shanghai 201804)
  • Online: 2021-12-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62163016, 62066014) and the Natural Science Foundation of Jiangxi Province (20212ACB202001, 20202BABL202018).

摘要: 人群计数旨在估计图像或视频中人群的数量、密度或分布,属于目标计数(object counting)领域的研究范畴,广泛应用于人群行为分析、公共安全管理之中,以便及时发现人群拥挤或异常行为,避免事故发生.鉴于人群计数系统强大的实用性,自21世纪以来,研究者对其方法及应用进行了大量广泛的研究.近年来,深度学习技术发展迅猛,很多工作发现深度学习技术可以有效地解决人群计数系统存在的一系列关键问题,例如跨场景计数、透视畸变、尺度变化等.因此,对基于深度学习的人群计数这一研究领域进行回顾、分析和展望.具体地,首先从概念、步骤、方法等维度详细介绍人群计数模型,分析基于传统方法和基于深度学习方法这2类人群计数模型的差异.然后,从计数网络结构、ground-truth生成、损失函数、评价指标这4个方面阐述基于深度学习的人群计数模型的研究现状.最后,比较分析了各种人群计数数据集的特点,并探讨和展望人群计数领域未来可能的研究方向.

关键词: 人群计数, 密度图估计, 多尺度, 深度学习, 卷积神经网络

Abstract: Crowd counting, aiming to estimate the number, density or distribution of crowds in images or videos, belongs to the research category of object counting. It has been widely employed in crowd behavior analysis and public safety management to detect crowding or abnormal behavior in time to avoid accidents. In the past decades, although tremendous efforts have been made to enhance the performance of crowd counting algorithms, some long-standing challenges, such as cross-scene counting, perspective distortion and scale variation, remain unresolved. Along this line, an emerging research trend is to exploit the deep learning technologies for crowd counting. It has been proven to be an effective way to address the above issues. In this paper, crowd counting models based on deep learning are reviewed, analyzed, and discussed. Firstly, crowd counting models are introduced in details from the perspective of their principles, steps, and model variants, and the difference between the crowd counting models based on traditional methods and the crowd counting models based on deep learning are analyzed. Then the research status of crowd counting based on deep learning are expounded from four aspects: network structure, ground-truth generation, loss function and evaluation index. Meanwhile, the characteristics of various crowd counting data sets are compared and analyzed. Finally, some future directions of crowd counting are given.

Key words: crowd counting, density map estimation, multi-scale, deep learning, convolutional neural network (CNN)

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