Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

In recent years there has been remarkable progress in one computer vision application
area: object detection. One of the most challenging and fundamental problems in object …

Understanding the world in 3D is a critical component of urban autonomous driving.
Generally, the combination of expensive LiDAR sensors and stereo RGB imaging has been …

Segmenting highly-overlapping objects is challenging, because typically no distinction is
made between real object contours and occlusion boundaries. Unlike previous two-stage …

Object detection generally requires sliding-window classifiers in tradition or anchor-based
predictions in modern deep learning approaches. However, either of these approaches …

Can our video understanding systems perceive objects when a heavy occlusion exists in a
scene? To answer this question, we collect a large-scale dataset called OVIS for occluded …

Pedestrian detection in a crowd is a very challenging issue. This paper addresses this
problem by a novel Non-Maximum Suppression (NMS) algorithm to better refine the …

We propose a simple yet effective proposal-based object detector, aiming at detecting highly-
overlapped instances in crowded scenes. The key of our approach is to let each proposal …

Pedestrian detection relying on deep convolution neural networks has made significant
progress. Though promising results have been achieved on standard pedestrians, the …

Security inspection often deals with a piece of baggage or suitcase where objects are
heavily overlapped with each other, resulting in an unsatisfactory performance for prohibited …