Hyperspectral object tracking is aimed at tracking targets by using both spatial information
and abundant spectral information, overcoming the drawbacks of traditional RGB tracking in …

In recent decades, remote sensing object counting has attracted increasing attention from
academia and industry due to its potential benefits in urban traffic, public safety, and road …

Supported by deep learning techniques, remote sensing scene classification, a fundamental
task in remote image analysis, has recently obtained remarkable progress. However, due to …

Although deep learning-based change detection (CD) methods achieve great success in
remote sensing images, they still suffer from two main challenges. First, popular …

An emerging Internet of Video Things (IoVT) application, crowd counting is a computer
vision task where the number of heads in a crowded scene is estimated. In recent years, it …

Object counting is a fundamental task in remote sensing analysis. Nevertheless, it has been
barely studied compared with object counting in natural images due to the challenging …

Most researchers focus on designing accurate crowd counting models with heavy
parameters and computations but ignore the resource burden during the model deployment …

It is important to determine car density in parking lots, especially in hospitals, large
enterprises, and residential areas, which are used intensively, in terms of executing existing …

Remote sensing object counting (RSOC) is finding applications in many fields. Global
regression is a long-ignored method for object counting, though it needs much less manual …

Object counting has attracted a lot of attention in remote-sensing image analysis. In density
map-based object counting algorithms, the ground-truth density maps generated by fix-sized …