Typical methods for pedestrian detection focus on either tackling mutual occlusions between
crowded pedestrians, or dealing with the various scales of pedestrians. Detecting …

Detecting small-scale pedestrians is one of the most challenging problems in pedestrian
detection. Due to the lack of visual details, the representations of small-scale pedestrians …

Pedestrian detection has achieved great improvements with the help of Convolutional
Neural Networks (CNNs). CNN can learn high-level features from input images, but the …

Pedestrian detection is a key problem for automatic driving, and the results have been
improved significantly via deep convolutional networks. However, there is still room to …

Despite significant research efforts in pedestrian detection over the past decade, there is still
a ten-fold performance gap between the state-of-the-art methods and human perception …

The research in pedestrian detection has made remarkable progress in recent years.
However, robust pedestrian detection in crowded scenes remains a considerable challenge …

Pedestrian detection has significantly progressed in recent years, thanks to the development
of DNNs. However, detection performance at occluded scenes is still far from satisfactory, as …

Large variance in instance scales will results in undesirable large intra-category variance in
features, which may severely hurt the performance of modern pedestrian detection (PD) …

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 …

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 …