Estimating count and density maps from crowd images has a wide range of applications
such as video surveillance, traffic monitoring, public safety and urban planning. In addition …

Recent advancements in intelligent surveillance systems for video analysis have been a
topic of great interest in the research community due to the vast number of applications to …

Previous work generally believes that improving the spatial invariance of convolutional
networks is the key to object counting. However, after verifying several mainstream counting …

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

We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-
quality crowd density and count estimation by explicitly incorporating global and local …

Estimating crowd count in densely crowded scenes is an extremely challenging task due to
non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded …

Studies show lots of advanced research on various data types such as image, speech, and
text using deep learning techniques, but nowadays, research on video processing is also an …

Crowd counting and crowd density estimation methods are of great significance in the field
of public security. Estimating crowd density and counting from single image or video frame …

Most of the crowd abnormal event detection methods rely on complex hand-crafted features
to represent the crowd motion and appearance. Convolutional Neural Networks (CNN) have …

When overpopulated cities face frequent crowded events like strikes, demonstrations,
parades or other sorts of people gatherings, they are confronted to multiple security issues …