The proliferation of vehicles and a subsequent increase in traffic accidents have led to a
heightened focus on driving safety. As a result, various researchers have been examining …

Computer Vision (CV) based surveillance in crowded scenes is one of the most significant
and promising topics due to the increase of people gatherings in public places where 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 …

Crowd counting in single-view images has achieved outstanding performance on existing
counting datasets. However, single-view counting is not applicable to large and wide scenes …

Multi-view crowd counting has been previously proposed to utilize multi-cameras to extend
the field-of-view of a single camera, capturing more people in the scene, and improve …

We propose a novel algorithm to identify occupied seats in a motor vehicle, ie, the number of
occupants and their positions, using a frequency modulated continuous wave radar. Instead …

In this paper, we tackle the problem of crowd counting in indoor videos, where people often
stay almost static for a long time. The label distribution, which covers a certain number of …

Counting people and things (targets) in a monitored area, also known as crowd-counting,
enables several applications in diverse scenarios, including smart building, intelligent …

Crowd counting has been studied for decades and a lot of works have achieved good
performance, especially the DNNs-based density map estimation methods. Most existing …

Deep learning based multi-view crowd counting (MVCC) has been proposed to handle
scenes with large size, in irregular shape or with severe occlusions. The current MVCC …