Human activity recognition is a key to a lot of applications such as healthcare and smart
home. In this study, we provide a comprehensive survey on recent advances and challenges …

With growing numbers of intelligent autonomous systems in human environments, the ability
of such systems to perceive, understand, and anticipate human behavior becomes …

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined
object categories. This restricted form of supervision limits their generality and usability since …

The main challenge of Multi-Object Tracking (MOT) lies in maintaining a continuous
trajectory for each target. Existing methods often learn reliable motion patterns to match the …

Recent works have shown that convolutional networks have substantially improved the
performance of multiple object tracking by simultaneously learning detection and …

Human behavior understanding with unmanned aerial vehicles (UAVs) is of great
significance for a wide range of applications, which simultaneously brings an urgent …

Deep learning-based object detection solutions emerged from computer vision has
captivated full attention in recent years. The growing UAV market trends and interest in …

Blind image super-resolution (SR), aiming to super-resolve low-resolution images with
unknown degradation, has attracted increasing attention due to its significance in promoting …

Predicting the future trajectories of multiple interacting pedestrians in a scene has become
an increasingly important problem for many different applications ranging from control of …

Pedestrian behavior anticipation is a key challenge in the design of assistive and
autonomous driving systems suitable for urban environments. An intelligent system should …