ABSTRACT
Estimating crowd counting from images is a difficult but important task given the large range of applications such as public safety, traffic monitoring, and urban planning. Occlusions, uneven density, variation in scale and perspective are all challenges in crowd analysis. Thanks to advancements in deep learning and constructing demanding databases, modern computer vision techniques have led to numerous cutting-edge methods that build the abilities needed to properly execute a wide range of scenarios. This article presents a brief description of pioneering methods based on hand-crafted representations, followed by an examination of contemporary approaches based on convolutional neural networks (CNNs) that have achieved significant performance. In addition, the most frequently utilized datasets are addressed, and lastly, promising research routes in this rapidly increasing area are indicated.
