Revisiting crowd counting: State-of-the-art, trends, and future perspectives

计算机科学 水准点(测量) 管道(软件) 人工智能 分类 数据科学 分类 机器学习 深度学习 资源(消歧) 情报检索 计算机网络 大地测量学 程序设计语言 地理
作者
Muhammad Asif Khan,Hamid Menouar,Ridha Hamila
出处
期刊:Image and Vision Computing [Elsevier BV]
卷期号:129: 104597-104597 被引量:37
标识
DOI:10.1016/j.imavis.2022.104597
摘要

Crowd counting is an effective tool for situational awareness in public places. Automated crowd counting using images and videos is an interesting yet challenging problem that has gained significant attention in computer vision. Over the past few years, various deep learning methods have been developed to achieve state-of-the-art performance. The methods evolved over time vary in many aspects such as model architecture, input pipeline, learning paradigm, computational complexity, and accuracy gains etc. In this paper, we present a systematic and comprehensive review of the most significant contributions in the area of crowd counting. Although few surveys exist on the topic, our survey is most up-to date and different in several aspects. First, it provides a more meaningful categorization of the most significant contributions by model architectures, learning methods (i.e., loss functions), and evaluation methods (i.e., evaluation metrics). We chose prominent and distinct works and excluded similar works. We also sort the well-known crowd counting models by their performance over benchmark datasets. We believe that this survey can be a good resource for novice researchers to understand the progressive developments and contributions over time and the current state-of-the-art.
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