Pedestrian Detection is the most critical module of an Autonomous Driving system. Although
a camera is commonly used for this purpose, its quality degrades severely in low-light night …

In the within-intersection area, vehicles from different approaches make turning movements
resulting in many conflict points. Hence, drivers are more prone to make mistakes in that …

Human pose estimation can be applied to many computer vision tasks, such as human–
computer interaction, motion recognition, and action detection. However, few previous …

The partially occluded image recognition (POIR) problem has been a challenge for artificial
intelligence for a long time. A common strategy to handle the POIR problem is using the non …

Multimodal learning, particularly for pedestrian detection, has recently received emphasis
due to its capability to function equally well in several critical autonomous driving scenarios …

The use of data and deep learning algorithms in transportation research have become
increasingly popular in recent years. Many studies rely on real-world data. Collecting …

Attention-only Transformers have been applied to solve Natural Language Processing
(NLP) tasks and Computer Vision (CV) tasks. One particular Transformer architecture …

Most of the existing works on pedestrian pose estimation do not consider estimating the
pose of an occluded pedestrian, as the annotations of the occluded parts are not available in …

Shadows are frequently encountered natural phenomena that significantly hinder the
performance of computer vision perception systems in practical settings, eg, autonomous …

Despite the remarkable success of multimodal models in automotive applications, their
practical benefits are often accompanied by a large number of parameters, including …