The crowded scenario not only contains instances at various scales but also introduces a
variety of occlusion patterns ranging from non-occluded situations to heavily occluded …

Pedestrian detection is an important branch of computer vision, and has important
applications in the fields of autonomous driving, artificial intelligence and video surveillance …

Detecting pedestrians accurately in urban scenes is significant for realistic applications like
autonomous driving or video surveillance. However, confusing human-like objects often …

Abstract Multiple Object Tracking (MOT) methods based on per-pixel prediction and
association have achieved remarkable progress recently. These approaches prefer to select …

End-to-end pedestrian detection focuses on training a pedestrian detection model via
discarding the Non-Maximum Suppression (NMS) post-processing. Though a few methods …

Pedestrian detection is still a challenging task for computer vision, especially in crowded
scenes where the overlaps between pedestrians tend to be large. The non-maximum …

The rapid advancement in text-to-video (T2V) generative models has enabled the synthesis
of high-fidelity video content guided by textual descriptions. Despite this significant progress …

The field of autonomous driving research has made significant strides toward achieving full
automation, endowing vehicles with self-awareness and independent decision making …

In recent years, several methods and datasets have been proposed to push the performance
of pedestrian detection in crowded scenarios. In this study, three crowd-specific detectors …

Pedestrian detection is a basic problem of computer vision, which has a wide range of
practical applications, such as autonomous driving and video intelligent surveillance …