Real-Time Computer Vision for Accident Detection and Prevention

in Special Issue   Posted on June 24, 2020 

Information for the Special Issue

Submission Deadline: Wed 30 Dec 2020
Journal Impact Factor : 1.968
Journal Name : Journal of Real-Time Image Processing
Journal Publisher:
Website for the Special Issue:
Journal & Submission Website:

Special Issue Call for Papers:

The rapid increase in population has predominantly increased the demand and usage of the motorized vehicles in all areas. This increase in motor vehicular usage has substantially increased the rate of road accidents in the recent decade. Furthermore, injuries, disabilities, and death due to fatal road accidents have been increasing every year despite the safety measures introduced for the public and private transportation system. Congestion of vehicles, a driver under alcohol or drug influence, distracted driving, street racing, faulty design of cars or traffic lights, tailgating, running red lights & stop signs, improper turns and driving in the wrong direction are some of the real causes of accidents across the globe. There are many advanced surveillance systems implemented for road safety, but there is much improvement room for prevention of accidents. The existing sophisticated vehicles monitoring and traffic surveillance systems can be used to prevent accidents from occurring. However, real-time observations are difficult with an enormous amount of surveillance data running continuously. With the emerging trends in the field of information and computer science, the use of innovative real-time technologies would help for accident prevention and detection.

Computer vision is the technology that is designed to imitate how the human visual system works. The digital image data from multiple surveillance systems are acquired in real-time and the data is analyzed. If there are any incidents such as speeding, reckless driving, accidents, etc., they are identified and reported by the systems concurrently. Image classification, object detection, object tracking, semantic segmentation, and instance segmentation are some of the computer vision-based techniques with advanced deep learning approaches which can be used for the real-time accident detection and prevention processes.

Similarly, using neural networks many anomalies can be detected in the movement of vehicles using historical data which can also be utilized in the prevention of accidents. The recent developments in the use of deep learning approaches in visual recognition has had a a significant impact on advanced computer vision research. Moreover, the assistance of computer vision in the surveillance of traffic for accident prevention and detection in real-time would be significant. This special issue on “Real-time computer vision for accident prevention and detection” attempts to bring the latest developments in real-time computer vision and image processing for the application of accident prevention and detection.

The list of relevant topics to this special issue includes, but not limited to, the following:

  • Real-time applications of computer vision and image analysis in traffic congestion
  • Real-time vision-based learning for accident and traffic collision reconstruction
  • Real-time computer vision-based statistical analysis of accident data
  • Real-time computer vision in road safety and intelligent traffic
  • Real-time embedded image/video processing for accident prevention
  • A Study on Real-time embedded system for accident prevention
  • Real-time applications of neural networks in transportation strategy planning and instinctive decision making
  • Real-time visual learning methods for risk-based accident prevention
  • Context-aware image sensor technologies for real-time road accident detection
  • Real-time studies on improved image segmentation and inspection for accidents
  • Architecture for real-time road inspection using multiple video surveillance systems
  • Advanced intelligent systems to prevent accidents using real-time video data
  • Challenges and risks in implementing computer vision in a real-time environment
  • Case studies on real-time computer vision-based accident detection

Guest Editors

Prof. Oscar Sanjuán Martínez (Lead Guest Editor), Universidad Internacional de la Rioja (UNIR), Logroño, Spain, [email protected]
Dr. Giuseppe Fenza, Department of Business Sciences – Management & Innovation Systems / DISA-MIS, University of Salerno,Fisciano SA, Italy, [email protected]
Prof. Ruben Gonzalez Crespo, Department of Engineering, School of Engineering and Technology, Universidad Internacional de la Rioja (UNIR), Logroño, Spain, [email protected]

Important Dates

Manuscript submission deadline:   30th December 2020
Authors notification:                         2nd March 2021
Revised manuscripts due:                 4th June 2021
Final notification:                              6th August 2021

Submission Guidelines

Authors from academia and industry working on the above research topics are invited to submit original manuscripts that have not been published and are not currently under review by other journals or conferences. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended.  At least 30% of new content is expected.
All the papers will be peer-reviewed according to the JRTIP reviewing procedure.

Special issues are reviewed on a “fast track” basis. Prior to sending full manuscript submissions, it is highly recommended to query the appropriateness of submission by sending a 100-200 words abstract to the guest editors whose contact information are provided above.

Paper submissions for the special issue should follow the submission format and guidelines of the journal ( Note that there is a page limit of 12 pages (double column format).

During the submission procedure in Editorial Manager (, authors should select ‘SI: Real-Time Computer Vision for Accident Detection and Prevention‘ at the submission step ‘Additional Information‘.

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