[Om-announce] [Deadline Extension] Special issue on Dense Surveillance System for Urban Traffic

[Fulvio Frati]

*** Deadline Extended: March 31, 2019 ***

 

[Apologies if you receive multiple copies of this CFP]

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Journal of Intelligent Transportation Systems: Technology, Planning, and
Operations

Special issue on Dense Surveillance System for Urban Traffic

http://explore.tandfonline.com/cfp/est/jmv06430-gits-cfp-si-dense-surveillan
ce-system-for-urban-traffic/ 

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Traffic congestion and safety are serious problems in modern cities.
Recurrent congestion happens during the morning/evening rush hours due to
demand exceeding capacity, while non-recurrent incident congestion can
happen due to unexpected events. Generally, about 40% of traffic congestion
is attributed to individual incidents including traffic collisions. In urban
areas, vehicle disablements and low-speed collisions on local and arterial
roads can cause traffic congestion, partly due to police response, e.g., to
address "fault" and related liability issues. Adverse weather and presence
of special events or peak hours can further exacerbate incident-induced
congestion and even cause secondary incidents. These problems can be at
least partially addressed by a "dense video surveillance system." Such
systems deploy cameras in close proximity along roads and at intersections
that capture real-time videos from different angles. If a low-speed traffic
collision (or a disablement) occurs, then the recordings can easily identify
the situation and help police respond efficiently as well as re-construct a
crash using video recordings. Traffic congestion can be reduced or avoided
through quick response by police and the dense surveillance system providing
a more complete video record of the incident to assist in identifying
traffic accident liability.

 

In this special issue, we will focus on the developments in dense video
surveillance systems and its applications in urban traffic. For such a video
surveillance system, camera nodes are densely deployed at intersections and
along roads. These nodes form a wireless sensor network, which can be energy
intensive. To provide energy-savings, low-complexity video coding and power
control can be used. Hence, we will look for contributions describing how
camera nodes can be robustly used stably and for longer time periods. In the
last few decades, Artificial Intelligence (AI) tools have greatly
contributed to Intelligent Transportation Systems (ITS) in general and to
traffic video analysis in particular, showing potential to solving hard
problems. Meanwhile, publication activity has accelerated on using deep
learning for traffic trajectory analysis and tracing. This special issue
hopes to discuss how new video surveillance systems can support key
functions needed to operate transportation systems.

 

This special issue will provide a highly recognized international forum for
presenting innovative developments of dense video surveillance systems
applied to avoiding urban traffic congestion. Note that traffic congestion
avoidance is not the only potential application of dense video surveillance
systems, and we also welcome papers on their application for improving
safety. The ultimate objective is to bring together well-focused, top
quality research contributions, providing the ITS research community an
opportunity to get an overall view of recent developments. The issue will
identify the most promising avenues and promote the visibility and relevance
of AI and wireless sensor network techniques. The intent is to raise
collective awareness of the domain of dense video surveillance as a
promising area to be pursued by the ITS research community.

 

* Extended Submission Deadline: March 31, 2019

 

* Topics Covered

- Applications of dense surveillance systems for traffic operations and
identifying or reconstructing traffic incidents on roadways

- Wireless sensor networks used in transportation systems

- Wireless communications applied in transportation

- Video capturing, compression and transmission

- 3D compression and analysis of traffic video

- Coding of multi-view traffic video

- Power control applications of traffic monitoring nodes

- Energy-saving coding of traffic monitoring nodes

- Low-complexity coding of traffic video

- Video analysis of transportation systems

- Vehicular trajectory analysis and tracing

- Deep learning applied in transportation

- Pattern recognition for transportation systems

 

* Editorial information

- Guest editor: Yong Fang, Chang'an University, China

- Guest editor: Gwanggil Jeon, Incheon National University, Korea

- Guest editor: Marco Anisetti, Universita' degli Studi di Milano, Italy

 

 




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