Call for Papers: IoT-driven Computer Vision Technology for Smart Transportation Applications 
As a big leap for humankind, the technological revolution has smartly influenced many sectors of the globe. For that instance, technologies like Big Data analytics, Deep Learning, the Internet of Things (IoT), Machine Learning, Artificial intelligence (AI), etc significantly has the potential to change our day-to-day lives. Over the recent years, IoT is gaining importance for its capacity of building a hyperconnected world with minimized human interaction. Seamlessly, the giant connection of things provides an improved platform for the integration of different devices to address explicit needs. Since this technology is upgrading in a rapid phase, it paves a way for an autonomous IoT infrastructure with ambient intelligence. As per the sensitiveness of the emerging IoT applications, intelligence could be offered at levels of devices, nodes, or cloud spaces. With the help of a machine and deep reinforcement learning capabilities that are inbuilt in the devices, the decision-making process could be made at a faster pace. On the other hand, computer vision is seen as an enabling AI technology that probably derives meaningful information from various visual inputs (like videos, digital images, etc.). Such a technology that makes the machine visualize an object/thing, requires a huge number of trained datasets. The combination of IoT and computer vision technology could support many applications like smart transportation, intelligent healthcare monitoring, online education, smart manufacturing, etc. Specifically, after this technological breakthrough intelligent transportation has obtained maximized benefits from them.
Consequently, the consumption of large information through IoT devices with the emerging computer vision technologies could pragmatically support various smart transportation applications. In that way, some applications of this transportation system may include intelligent traffic and parking management, collision avoidance systems, automated road speed enforcement, e-toll collections, etc. In spite of having so many benefits, there are still some disadvantages that circumvent this architecture. Commencing from increased need for on-time monitoring, lack of huge data storage facilities, difficulties in understanding mixed traffic, prone to security and privacy threats, increased vulnerabilities to uncertainties, etc are a few glitches that are found in this system. To have a better infrastructure with full efficiency it is required to eliminate issues that are existing.
Researchers, scholars, academicians, and other industry stakeholders from this area are welcome to submit their high-quality research works for improving this current transportation infrastructure. Thus, the purpose of this special issue is to invite both academic and industrial participants to deliver innovative solutions for the betterment of this domain
Topics of interest include, but are not limited to:
- Real-time and dynamic prediction of traffic flows using IoT and machine learning capabilities
- Smart transportation architectures with IoT and self-adaptive AI algorithms
- IoT and Green AI-empowered pollution control solutions for smart transportation
- Role of deep learning, AI, and IoT for connected self-driving vehicles
- Implications HCI and computer vision technologies for optimizing smart transportation system
- Embedded smart sensor networks for computer vision-enabled intelligent transportation system
- Self-organizing smart transportation system using cloud-based IoT technology
- IoT -driven big data analytics for collision avoidance system in smart transportation
- Trends in computer vision-enabled robotics for intelligent transportation systems
- Smart public transportation system with deep reinforcement learning and computer vision technology
Dr. Maria Trocan | First Co-Guest Editor
Institut Supérieur d'Électronique de Paris (ISEP), Paris, France
Dr. Behçet Uğur Töreyin | Second Co-Guest Editor
Istanbul Technical University (ITU), Istanbul, Turkey
Paper Submission Deadline: September 30, 2022
Author Notification: December 31, 2022
Revised Papers Submission: February 28, 2023
Final Acceptance: April 01, 2023
Authors should prepare their manuscript according to the Instructions for Authors available from the Multimedia Tools and Applications website. Authors should submit through the online submission site at https://www.editorialmanager.com/mtap/default.aspx and select “SI 1231 - IoT-driven Computer Vision Technology for Smart Transportation Applications” when they reach the “Article Type” step in the submission process. Submitted papers should present original, unpublished work, relevant to one of the topics of the special issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process.