A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …

Crowd counting is an effective tool for situational awareness in public places. Automated
crowd counting using images and videos is an interesting yet challenging problem that has …

We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++)
that contains “4,372” images with “1.51 million” annotations. In comparison to existing …

Crowd counting is a fundamental yet challenging task, which desires rich information to
generate pixel-wise crowd density maps. However, most previous methods only used the …

For a globally recognized plant breeding organization, manually recorded field observation
data is crucial for plant breeding decision making. However, certain phenotypic traits such …

In this article, we propose a novel self-training approach named Crowd-SDNet that enables
a typical object detector trained only with point-level annotations (ie, objects are labeled with …

In this paper, we present an analysis of important aspects that arise during the development
of neural network applications. Our aim is to determine if the choice of library can impact the …

To simultaneously estimate the number of heads and locate heads with bounding boxes, we
resort to detection-based crowd counting by leveraging RGB-D data and design a dual-path …

In this paper, we propose two modified neural networks based on dual path multi-scale
fusion networks (SFANet) and SegNet for accurate and efficient crowd counting. Inspired by …

Recent crowd counting approaches have achieved excellent performance. However, they
are essentially based on fully supervised paradigm and require large number of annotated …