TY  - EJOU
AU  - Wang, Jianyong 
AU  - Gao, Mingliang 
AU  - Li, Qilei 
AU  - Kim, Hyunbum 
AU  - Jeon, Gwanggil 

TI  - A Survey on Supervised, Unsupervised, and Semi-Supervised Approaches in Crowd Counting
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 81
IS  - 3
SN  - 1546-2226

AB  - Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety. Crowd counting has attracted considerable attention in the field of computer vision, leading to the development of numerous advanced models and methodologies. These approaches vary in terms of supervision techniques, network architectures, and model complexity. Currently, most crowd counting methods rely on fully supervised learning, which has proven to be effective. However, this approach presents challenges in real-world scenarios, where labeled data and ground-truth annotations are often scarce. As a result, there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical applications. This paper offers a comprehensive review of crowd counting models, with a particular focus on semi-supervised and unsupervised approaches based on their supervision paradigms. We summarize and critically analyze the key methods in these two categories, highlighting their strengths and limitations. Furthermore, we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark datasets. We believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.
KW  - Crowd counting; density estimation; convolutional neural network (CNN); un/semi-supervised learning

DO  - 10.32604/cmc.2024.058637