Traditional handcrafted crowd-counting techniques in an image are currently transformed
via machine-learning and artificial-intelligence techniques into intelligent crowd-counting …

Localizing individuals in crowds is more in accordance with the practical demands of
subsequent high-level crowd analysis tasks than simply counting. However, existing …

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 …

Accurately estimating the number of objects in a single image is a challenging yet
meaningful task and has been applied in many applications such as urban planning and …

To promote the developments of object detection, tracking and counting algorithms in drone-
captured videos, we construct a benchmark with a new drone-captured large-scale dataset …

The aim of crowd counting is to estimate the number of people in images by leveraging the
annotation of center positions for pedestrians' heads. Promising progresses have been …

Tremendous variation in the scale of people/head size is a critical problem for crowd
counting. To improve the scale invariance of feature representation, recent works …

Crowd monitoring and analysis has become increasingly used for unmanned aerial vehicle
applications. From preventing stampede in high concentration crowds to estimating crowd …

State-of-the-art methods for counting people in crowded scenes rely on deep networks to
estimate crowd density. While effective, these data-driven approaches rely on large amount …

The objective of this paper is to rectify any monocular image by computing a homography
matrix that transforms it to a geometrically correct bird's eye (overhead) view. We make the …