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Approaches on crowd counting and density estimation: a review

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Abstract

In recent years, urgent needs for counting crowds and vehicles have greatly promoted research of crowd counting and density estimation. Benefiting from the rapid development of deep learning, the counting performance has been greatly improved, and the application scenarios have been further expanded. Aiming to deeply understand the development status of crowd counting and density estimation, we introduce and analyze the typical methods in this field and especially focus on elaborating deep learning-based counting methods. We summarize the existing approaches into four categories, i.e., detection-based, regression-based, convolutional neural network based and video-based. Each category is explicated in great detail. To provide more concrete reference, we compare the performance of typical methods on the popular benchmarks. We further elaborate on the datasets and metrics for the crowd counting community and discuss the work of solving the problem of small-sample-based counting, dataset annotation methods and so on. Finally, we summarize various challenges facing crowd counting and their corresponding solutions and propose a set of development trends in the future.

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Authors and Affiliations

  1. School of Electromechanical Engineering, Beijing Information Science and Technology University, Beijing, 100192, China

    Bo Li, Peiwen Liu & Cheng Liu

  2. Computer School, Beijing Information Science and Technology University, Beijing, 100192, China

    Hongbo Huang

  3. Institute of Computing Intelligence, Beijing Information Science and Technology University, Beijing, 100192, China

    Hongbo Huang

  4. School of Information Management, Beijing Information Science and Technology University, Beijing, 100192, China

    Ang Zhang

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  1. Bo Li
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  2. Hongbo Huang
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  3. Ang Zhang
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  4. Peiwen Liu
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  5. Cheng Liu
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Correspondence to Hongbo Huang.

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Li, B., Huang, H., Zhang, A. et al. Approaches on crowd counting and density estimation: a review. Pattern Anal Applic 24, 853–874 (2021). https://doi.org/10.1007/s10044-021-00959-z

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