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    余鹰, 朱慧琳, 钱进, 潘诚, 苗夺谦. 基于深度学习的人群计数研究综述[J]. 计算机研究与发展, 2021, 58(12): 2724-2747. DOI: 10.7544/issn1000-1239.2021.20200699
    引用本文: 余鹰, 朱慧琳, 钱进, 潘诚, 苗夺谦. 基于深度学习的人群计数研究综述[J]. 计算机研究与发展, 2021, 58(12): 2724-2747. DOI: 10.7544/issn1000-1239.2021.20200699
    Yu Ying, Zhu Huilin, Qian Jin, Pan Cheng, Miao Duoqian. Survey on Deep Learning Based Crowd Counting[J]. Journal of Computer Research and Development, 2021, 58(12): 2724-2747. DOI: 10.7544/issn1000-1239.2021.20200699
    Citation: Yu Ying, Zhu Huilin, Qian Jin, Pan Cheng, Miao Duoqian. Survey on Deep Learning Based Crowd Counting[J]. Journal of Computer Research and Development, 2021, 58(12): 2724-2747. DOI: 10.7544/issn1000-1239.2021.20200699

    基于深度学习的人群计数研究综述

    Survey on Deep Learning Based Crowd Counting

    • 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.

       

      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.

       

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