With multiple crowd gatherings of millions of people every year in events ranging from
pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd …

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 …

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 …

In this work, we tackle the problem of crowd counting in images. We present a Convolutional
Neural Network (CNN) based density estimation approach to solve this problem. Predicting …

Our work proposes a novel deep learning framework for estimating crowd density from static
images of highly dense crowds. We use a combination of deep and shallow, fully …

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

Crowd counting is a highly challenging problem in computer vision and machine learning.
Most previous methods have focused on consistent density crowds, ie, either a sparse or a …

To fully leverage the data captured from different scenes with different view angles while
reducing the annotation cost, this paper studies a novel crowd counting setting, ie only using …

Crowd counting presents enormous challenges in the form of large variation in scales within
images and across the dataset. These issues are further exacerbated in highly congested …

Crowd counting is a challenging task in the presence of drastic scale variations, the clutter
background, and severe occlusions, etc. Existing CNN-based counting methods tackle these …