Crowd counting in congested scenarios is an essential yet challenging task in detecting
abnormal crowd for contemporary urban planning. The counting accuracy has been …

Abstract The Internet of Things (IoT) provides a collaborative infrastructure to communicate
smart devices with cloud-edge healthcare applications, medical devices, wearable …

The task of crowd counting and localization is to predict the count and position of people in a
crowd, which is a practical and essential subtask in crowd analysis and smart city systems …

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 …

Abstract Currently, state-of-the-art crowd counting algorithms rely excessively on location-
level annotations, which are burdensome to acquire. When only weak supervisory signals at …

Crowd counting in adverse hazy weather is inevitable and significant to the scene
understanding in real-world application. For the crucial and challenging hazy-weather crowd …

The integration of remote sensing object counting in the Mobile Edge Computing (MEC)
environment is of crucial significance and practical value. However, the presence of …

Automatically counting fish in sonar images has been attracting increasing attention in
recent years because extreme efforts are needed in manual counting. Density map …

With the rapid advancement of deep learning, the performance of crowd counting has
improved significantly. Nonetheless, existing crowd counting models primarily depend on a …

Deep Multi-view Subspace Clustering is a powerful unsupervised learning technique for
clustering multi-view data, which has achieved significant attention during recent decades …