In recent years, with the development of computing power and deep learning algorithms,
pedestrian detection has made great progress. Nevertheless, once a detection model …

As a special type of object detection, pedestrian detection in generic scenes has made a
significant progress trained with large amounts of labeled training data manually. While the …

State-of-the-art pedestrian detection models have achieved great success in many
benchmarks. However, these models require lots of annotation information and the labeling …

To solve the problem of numerous deep convolutions in YOLOv4, which generates many
redundant background features so that it cannot focus on pedestrians at a specific scale, we …

Pedestrian detection is a challenging research task, and it is widely applied in automatic
driving and intelligent surveillance fields. Although many approaches based on deep …

Pedestrian detection is a crucial task in many vision-based applications, such as video
surveillance, human activity analysis and autonomous driving. Recently, most of the existing …

Abstract Recently, Faster R-CNN achieves great performance in deep learning based object
detection. However, a major bottleneck of Faster R-CNN lies on the sharp performance …

Pedestrian detection is a canonical problem in computer vision. Motivated by the
observation that the major bottleneck of pedestrian detection lies on the different scales of …

Pedestrian detection is used in many vision based applications ranging from video
surveillance to autonomous driving. Despite achieving high performance, it is still largely …

Pedestrian detection is used in many vision based applications ranging from video
surveillance to autonomous driving. Despite achieving high performance, it is still largely …