Pedestrian detection in the wild remains a challenging problem especially when the scene
contains significant occlusion and/or low resolution of the pedestrians to be detected …

We present a unified multi-task learning architecture for fast and accurate pedestrian
detection. Different from existing methods which often focus on either a new loss function or …

Pedestrian detection in the wild remains a challenging problem especially for scenes
containing serious occlusion. In this paper, we propose a novel feature learning method in …

Pedestrian detection has attracted more attention in the fields of computer vision and
artificial intelligence. A variety of real-world applications involving pedestrian detection have …

Pedestrian detection is a crucial task in intelligent transportation systems, which can be
applied in autonomous vehicles and traffic scene video surveillance systems. The past few …

Abstract Though Faster R-CNN based two-stage detectors have witnessed significant boost
in pedestrian detection accuracy, it is still slow for practical applications. One solution is to …

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 …

Serious scale variation is a key challenge in pedestrian detection. Most works typically
employ a feature pyramid network to detect objects at diverse scales. Such a method suffers …

Recently, pedestrian detection has made significant advances benefiting from the region-
based convolutional neural networks (R-CNN). However, training R-CNN with a holistic …

High resolution and strong semantic representation are both vital for feature extraction
networks of pedestrian detection. The existing high-resolution network (HRNet) has …