This paper explores deep learning (DL) methods that are used or have the potential to be
used for traffic video analysis, emphasising driving safety for both autonomous vehicles and …

Computer vision applications in intelligent transportation systems (ITS) and autonomous
driving (AD) have gravitated towards deep neural network architectures in recent years …

Due to the advancement of camera hardware and machine learning techniques, video
object tracking for surveillance has received noticeable attention from the computer vision …

Detecting abnormal trajectories is an important task in research and industrial applications,
which has attracted considerable attention in recent decades. This work studies the existing …

The road traffic scenes are usually complex and the traffic video is vulnerable to external
factors such as light, weather, and obstructions. It is difficult to extract the traffic parameters …

This paper focuses on the aspect of traffic lights control in order to reduce congestion and
thereby contribute to the reduction of the negative impact of transport on the urban …

The paper presents the analysis of the impact of various drivers' behaviours on the traffic
flow in the city. The analysis of the countdown timers mounted at traffic lights and their …

Video surveillance systems are essential for an intelligent and smart traffic monitoring
system. Detecting and recognizing traffic anomalies is the foremost task for the safety and …

In this paper we analyze geofencing technology in inland mobile navigation systems. We
describe implementation issues related to proximity analysis used for safety and …

Surveillance systems are important to monitor and control critical situations for the field of
traffic flow. Our aim is to enhance the ITS system about highway traffic and retrograde …