Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …

The prevailing applications of Unmanned Aerial Vehicles (UAVs) in transportation systems
promote the development of object detection methods to collect real-time traffic information …

In the present study, towards reliable and efficient object detection in thermal infrared (TIR)
images, we put forward a novel object detection approach, termed TIRNet, which is built …

Convolution neural networks (CNN) have achieved great success in natural image
processing where large amounts of training data are available. However, for the polarimetric …

Single-person human pose estimation facilitates markerless movement analysis in sports, as
well as in clinical applications. Still, state-of-the-art models for human pose estimation …

The Siamese tracking framework has attracted much attention due to its scalability and
efficiency in recent years. However, it is less effective in recognizing arbitrary targets with …

Recent advancements in the field of visual tracking have been propelled by the
amalgamation of Siamese networks and region proposal networks, which have …

Detecting small objects is a challenging job for the single-shot multibox detector (SSD)
model due to the limited information contained in features and complex background …

Annually, deep learning algorithms have proven their effectiveness in many vision-based
applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In …

During automatic driving, the complex background and mutual occlusion between multiple
targets hinder the correct judgment of the detector and miss detection. When a close-range …