Accurate and consistent vulnerable road user detection remains one of the most challenging
perception tasks for autonomous vehicles. One of the most complex outstanding issues is …

The crowded scenario not only contains instances at various scales but also introduces a
variety of occlusion patterns ranging from non-occluded situations to heavily occluded …

Pedestrian detection in crowded scenes is a challenging problem since the pedestrians
often gather together and occlude each other. In this paper, we propose a new occlusion …

Pedestrian detection, as a research hotspot in the field of computer vision, is widely used in
many fields, such as automatic driving, video surveillance, robots and so on. In recent years …

In this paper, we propose a graininess-aware deep feature learning method for pedestrian
detection. Unlike most existing pedestrian detection methods which only consider low …

Occlusions present a great challenge for pedestrian detection in practical applications. In
this paper, we propose a novel approach to simultaneous pedestrian detection and …

Pedestrian detection has achieved significant progress with the availability of existing
benchmark datasets. However, there is a gap in the diversity and density between real world …

Pedestrian detection is widely applied in surveillance, autonomous robotic navigation, and
automotive safety. However, there are many occlusion problems in real life. This paper …

Detecting pedestrians that are partially occluded remains a challenging problem due to
variations and uncertainties of partial occlusion patterns. Following a commonly used …

State-of-the-art pedestrian detectors have performed promisingly on non-occluded
pedestrians, yet they are still confronted by heavy occlusions. Although many previous …