Crowd counting is an application-oriented task and its inference efficiency is crucial for real-
world applications. However, most previous works relied on heavy backbone networks and …

State-of-the-art crowd counting models follow an encoder-decoder approach. Images are
first processed by the encoder to extract features. Then, to account for perspective distortion …

Recently, counting the number of people for crowd scenes is a hot topic because of its
widespread applications (eg video surveillance, public security). It is a difficult task in the …

This paper proposes to handle the practical problem of learning a universal model for crowd
counting across scenes and datasets. We dissect that the crux of this problem is the …

Crowd counting is a difficult task because of the diversity of scenes. Most of the existing
crowd counting methods adopt complex structures with massive backbones to enhance the …

Automatic estimation of the number of people in unconstrained crowded scenes is a
challenging task and one major difficulty stems from the huge scale variation of people. In …

To learn a reliable people counter from crowd images, head center annotations are normally
required. Annotating head centers is however a laborious and tedious process in dense …

Crowd counting from unconstrained scene images is a crucial task in many real-world
applications like urban surveillance and management, but it is greatly challenged by the …

Crowd counting is the task of estimating people numbers in crowd images. Modern crowd
counting methods employ deep neural networks to estimate crowd counts via crowd density …

Abstract Convolutional Neural Network (CNN) based methods generally take crowd
counting as a regression task by outputting crowd densities. They learn the mapping …