Object detection, as of one the most fundamental and challenging problems in computer
vision, has received great attention in recent years. Over the past two decades, we have …

With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for
object detection. While almost all state-of-the-art object detectors utilize predefined anchors …

As the most basic protection for workers, safety helmets have great significance to workers'
lives. However, due to a lack of safety awareness, safety helmets are often not worn. With …

Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …

Pedestrian detection in crowded scenes is a challenging problem since the pedestrians
often gather together and occlude each other. In this paper, we propose a new occlusion …

Pedestrian detection and tracking have become an important field in the computer vision
research area. This growing interest, started in the last decades, might be explained by the …

Current methods for skeleton-based human action recognition usually work with complete
skeletons. However, in real scenarios, it is inevitable to capture incomplete or noisy …

Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

Almost all of the current top-performing object detection networks employ region proposals
to guide the search for object instances. State-of-the-art region proposal methods usually …