Drone deployment has become crucial in a variety of applications, including solutions to
traffic issues in metropolitan areas and highways. On the other hand, data collected via …

Object detection using deep learning has attracted considerable interest from researchers
because of its competency in performing state-of-the-art tasks, including detection …

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

We show that crowd counting can be viewed as a decomposable point querying process.
This formulation enables arbitrary points as input and jointly reasons whether the points are …

Scale variation is a deep-rooted problem in object counting, which has not been effectively
addressed by existing scale-aware algorithms. An important factor is that they typically …

Class-agnostic counting (CAC) aims to count all instances in a query image given few
exemplars. A standard pipeline is to extract visual features from exemplars and match them …

Perspective distortions and crowd variations make crowd counting a challenging task in
computer vision. To tackle it, many previous works have used multi-scale architecture in …

Due to its promising results density map regression has been widely employed for image-
based crowd counting. The approach however often suffers from severe performance …

Crowd localization is a new computer vision task, evolved from crowd counting. Different
from the latter, it provides more precise location information for each instance, not just …

Presently, to obtain a more accurate density map and crowd number, existing methods often
count by combining training RGB images and depth images. However, these methods are …