Pedestrian detection is the cornerstone of many vision based applications, starting from
object tracking to video surveillance and more recently, autonomous driving. With the rapid …

Pedestrian detection is receiving more attention with the development of deep learning and
smart driving technology. However, the performance of existed pedestrian detection …

For many pedestrian detectors, background vs. foreground errors heavily influence the
detection quality. Our main contribution is to design semantic regions of interest that extract …

One of the most fundamental challenges in computer vision is pedestrian detection since it
involves both the classification and localization of pedestrians at a location. To achieve real …

Pedestrian detection is highly valued in intelligent surveillance systems. Most existing
pedestrian datasets are autonomously collected from non-surveillance videos, which result …

The existing real-time pedestrian detection method often loses part of the detection
accuracy. For this reason, we proposed a real-time pedestrian detection algorithm based on …

Several or even dozens of times spatial scale variation is one of the major bottleneck for
pedestrian detection. Although the Region-based Convolutional Neural Network (R-CNN) …

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

This paper presents a pedestrian detection approach that uses neural features from a fully
convolutional network (FCN) instead of features manually designed. We train an AdaBoost …

Pedestrian detection using deep convolutional neural networks (DCNNs) has made a
breakthrough in the last few years and researchers have proposed different DCNN …