Abstract
Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring. In addition, methods developed to estimate the number of people can be adapted and applied to related tasks in various fields, such as plant counting, vehicle counting, and cell microscopy. Many challenges and problems face crowd counting, including cluttered scenes, extreme occlusions, scale variation, and changes in camera perspective. Therefore, in the past few years, tremendous research efforts have been devoted to crowd counting, and numerous excellent techniques have been proposed. The significant progress in crowd counting methods in recent years is mostly attributed to advances in deep convolution neural networks (CNNs) as well as to public crowd counting datasets. In this work, we review the papers that have been published in the last decade and provide a comprehensive survey of the recent CNNs based crowd counting techniques. We briefly review detection-based, regression-based, and traditional density estimation based approaches. Then, we delve into detail regarding the deep learning based density estimation approaches and recently published datasets. In addition, we discuss the potential applications of crowd counting and in particular its applications using unmanned aerial vehicle (UAV) images.
Highlights
Wang et al [48] proposed SCNet, which is a compact single-column architecture for crowd counting. It consists of three modules: residual fusion modules (RFM) to extract multi-scale features, a pyramid pooling module (PPM) to combine features at different stages, and a sub-pixel convolutional module (SPCM) followed by an upsampling layer to recover the resolution
With the increasing development of crowd counting approaches, numerous datasets have been proposed over the last decade to drive research on crowd counting and develop models to deal with various limitations including changes in perspective and scale, variation in light conditions, crowd density, cluttering, and severe occlusion
We report results of recent traditional approaches along with the convolution neural networks (CNNs)-based methods on the most popular datasets
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The increasing growth of the world population and the development of urbanization has resulted in frequent crowd gatherings in numerous activities, such as stadium events, political events, and festivals (See Figure 1). In this context, crowd counting and density estimation are crucial for a better control & management and to ensure the security and the safety of the public. A section in this paper is dedicated to reviewing the most important techniques that can be extended to develop potential applications using unmanned aerial vehicle (UAV) images.
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