Recent advances in label assignment in object detection mainly seek to independently
define positive/negative training samples for each ground-truth (gt) object. In this paper, we …

Mainstream object detectors based on the fully convolutional network has achieved
impressive performance. While most of them still need a hand-designed non-maximum …

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 …

In this paper, we propose a new query-based detection framework for crowd detection.
Previous query-based detectors suffer from two drawbacks: first, multiple predictions will be …

Pedestrian detection is an important but challenging problem in computer vision, especially
in human-centric tasks. Over the past decade, significant improvement has been witnessed …

The occurrence of forest fires can lead to ecological damage, property loss, and human
casualties. Current forest fire smoke detection methods do not sufficiently consider the …

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

Multispectral pedestrian detection is an important and valuable task in many applications,
which could provide a more accurate and reliable pedestrian detection result by using the …

Vehicles, pedestrians, and riders are the most important and interesting objects for the
perception modules of self-driving vehicles and video surveillance. However, the state-of-the …