Computer Vision Applications in Intelligent Transportation Systems: A Survey.

Area of Science:

• Engineering
• Computer Science
• Transportation Science

Background:

• Computer vision (CV) is increasingly vital in intelligent transportation systems (ITS).
• CV applications aim to boost transportation efficiency, intelligence, and safety.
• Advances in CV offer solutions for traffic monitoring, incident detection, and road condition assessment.

Purpose of the Study:

• To survey existing computer vision applications within the intelligent transportation systems domain.
• To analyze machine learning and deep learning methods employed in ITS.
• To provide a holistic overview of CV's role in advancing ITS.

Main Methods:

• Literature review of computer vision applications in ITS.
• Analysis of machine learning and deep learning techniques used.
• Examination of applicability, advantages, and challenges of CV in ITS.

Main Results:

• CV significantly contributes to traffic monitoring, control, and incident management.
• Deep learning and machine learning methods are key enablers for CV in ITS.
• CV offers substantial benefits for ITS but also presents implementation challenges.

Conclusions:

• Computer vision is crucial for developing smarter and safer intelligent transportation systems.
• Further research is needed to overcome challenges and explore future trends in CV for ITS.
• This review synthesizes current knowledge to guide future advancements in the field.