A comprehensive survey of crowd density estimation and counting

Abstract

Crowd counting is one of the important and challenging research topics in computer vision. In recent years, with the rapid development of deep learning, the model architectures, learning paradigms, and counting accuracy have undergone significant changes. To help researchers quickly understand the research progress in this area, this paper presents a comprehensive survey of crowd density estimation and counting approaches. Initially, the technical challenges and commonly used datasets are intoroduced for crowd counting. Crowd counting approaches is them categorized into two groups based on the feature extraction methods employed: traditional approaches and deep learning-based approaches. A systematic and focused analysis of deep learning-based approaches is proposed. Subsequently, some training and evaluation details are introduced, including labels generation, loss functions, supervised training methods, and evaluation metrics. The accuracy and robustness of selected classical models are further compared. Finally, future prospects, strategies, and challenges are discussed for crowd counting. This review is comprehensive and timely, stemming from the selection of prominent and unique works.