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 …

Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an
essential role in many real-world applications. Although having enjoyed the merits of deep …

Current mainstream pedestrian detectors tend to profit directly from convolutional neural
networks (CNNs) that are designed for image classification. While requiring a large …

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

Pedestrian detection plays an important role in some areas such as autonomous driving, but
due to heavy occlusion and various scales, it is still challenging. In this article, we propose …

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 relying on deep convolution neural networks has made significant
progress. Though promising results have been achieved on standard pedestrians, the …

Detecting pedestrians is a challenging computer vision task, especially in the intelligent
transportation system. Mainstream pedestrian detection methods purely utilize information of …

Our observation shows that pedestrians' heights greatly affect pedestrian detection
performance, and for small pedestrians, their context information is useful for localizing and …