The huge amount of data generated by the Internet of Things (IoT) devices needs the
computational power and storage capacity provided by cloud, edge, and fog computing …

Over the past decade, vision-based vehicle detection techniques for road safety
improvement have gained an increasing amount of attention. Unfortunately, the techniques …

In this paper, we develop deep spatio-temporal neural networks to sequentially count
vehicles from low quality videos captured by city cameras (citycams). Citycam videos have …

The need to accurately estimate the speed of road vehicles is becoming increasingly
important for at least two main reasons. First, the number of speed cameras installed …

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 …

A crime is a deliberate act that can cause physical or psychological harm, as well as
property damage or loss, and can lead to punishment by a state or other authority according …

Understanding traffic density from large-scale web camera (webcam) videos is a
challenging problem because such videos have low spatial and temporal resolution, high …

Vehicle detection in traffic surveillance images is an important approach to obtain vehicle
data and rich traffic flow parameters. Recently, deep learning based methods have been …

The rapid advancement in the field of deep learning and high performance computing has
highly augmented the scope of video-based vehicle counting system. In this paper, the …

Vehicle classification has evolved into a significant subject of study due to its importance in
autonomous navigation, traffic analysis, surveillance and security systems, and …