Crowd detection and counting are important tasks in several applications of crowd analysis
including traffic management, public safety and event planning. Automatic crowd counting …

Most existing crowd counting approaches use limited information of RGB (red–green–blue)
images and fail to suitably extract potential pedestrians in unconstrained scenarios …

The central problem of text-based person retrieval is how to properly bridge the gap
between heterogeneous cross-modal data. Many of the previous works contrive to learn a …

Great efforts have been devoted to solving the crowd counting problem based on vision or
other fine-grained measurements. Popular vision and WiFi channel state information based …

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

With the rapid development of wireless technologies, many researchers use WiFi signals for
human action recognition. Most of the previous methods are based on traditional machine …

The skeleton-based human action recognition has become a popular research focus due to
its promising applications. The current methods that model skeletons as spatial–temporal …

Text-based person re-identification aims to retrieve images of the corresponding person
from a large visual database according to a natural language description. When it comes to …

Crowd counting in congested urban transport systems is a highly challenging task for
computer vision and deep learning due to several factors such as mutual occlusion …

Cross-modal human pose estimation has a wide range of applications. Traditional image-
based pose estimation will not work well in poor light or darkness. Therefore, some sensors …