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 …

Abstract Although the Region-based Convolutional Neural Network (R-CNN) families have
shown promising results for object detection, they still face great challenges for task-specific …

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 …

Deep Learning methods have achieved great successes in pedestrian detection owing to
their ability of learning discriminative features from pixel level. However, most of the popular …

The CNN based pedestrian detection is developing rapidly in recent years. Compared to the
features used in former pedestrian detection models, the features from deep CNN have …

We propose a guiding network to assist with training a deep convolutional neural network
(DCNN) to improve the accuracy of pedestrian detection. The guiding network is adaptively …

Pedestrian detection is a canonical sub-problem of object detection with high demand
during recent years. Although recent deep learning object detectors such as Fast/Faster R …

In recent years, with the research enthusiasm of deep learning, pedestrian detection has
made significant progress. However, the performance of state-of-the-art algorithms are still …

This paper proposes boosting-like deep learning (BDL) framework for pedestrian detection.
Due to overtraining on the limited training samples, overfitting is a major problem of deep …

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 …