Researchers from Incheon National University unveil smart traffic monitoring system

Researchers-from-Incheon-National-University-unveil-smart-traffic-monitoring-systems

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Researchers at Incheon National University have developed a smart traffic monitoring system using adaptive cameras.

The system dynamically activates more cameras during busy times and fewer during quiet periods, optimising resource use and improving road safety.

Urban traffic management

The University found that after the system was tested in diverse scenarios, it showed the potential to reduce accidents, ease congestion and conserve energy, making it a promising solution for smarter urban traffic management.

Effective urban traffic management remains a cornerstone of smart city development.

With the rise of autonomous vehicles and connected transportation systems, dynamic surveillance solutions are critical to ensuring smooth traffic flow, minimising accidents and optimising efficiency.

However, traditional static camera setups often fail to adapt to rapid changes in traffic conditions, resulting in inefficient monitoring and resource use.

Incheon National University’s solution

To address this issue, researchers from Incheon National University, led by Associate Professor Hyunbum Kim, have introduced a solution: an augmented fluid surveillance system – designed to adapt in real-time to varying traffic scenarios.

The innovative system employs a network of single-lens cameras arranged in a dynamic grid.

This model intelligently adjusts its surveillance coverage by activating or deactivating cameras based on real-time traffic conditions, ensuring efficient and flexible monitoring.

“Revolutionise traffic management”

Dr Kim, the Lead Researcher, explained: “Our motivation stems from the growing need for adaptive traffic monitoring systems that can handle diverse and unpredictable scenarios.

“By creating an augmented fluid surveillance system, we aim to revolutionise traffic management and provide seamless intelligent transportation services,” Dr Kim added.

The study

The study formalised the “Augmented Fluid Surveillance Efficiency Maximisation Problem” (MaxAugmentFluSurv).

This problem focuses on finding the best way to place and use cameras for maximum efficiency while still covering all necessary areas.

The researchers came up with two smart solutions to address this challenge:

The first approach

  • Called the ‘Random-Value-Camera-Level Algorithm’, organises cameras in a 3×3 grid
  • Some cameras are always on to ensure basic coverage, while others switch on or off depending on traffic levels
  • This way, during busy times, more cameras turn on to monitor the situation and during quiet times, fewer cameras are active, saving energy

The second approach

  • Called the ALL-Random-With-Weight Algorithm, works similarly but is even more flexible
  • It assigns a unique role to each camera based on its position in the grid
  • Cameras in key positions stay active all the time, while others adjust their activity to match traffic conditions
  • This method ensures a balance between thorough monitoring and efficient energy use

Effectiveness

Extensive simulations showed these methods worked effectively under different conditions, such as varying traffic levels, slopes and angles.

The system reduced energy use during low traffic and provided strong coverage during peak hours by predicting and adjusting to traffic patterns.

Dr Kim later highlighted: “Our approach optimizes camera usage and saves energy while ensuring reliable surveillance. It’s a step toward smarter and more eco-friendly traffic management.”

Alternative uses

Beyond traffic control, this adaptive system could also be used for:

  • Crowd monitoring
  • Disaster response
  • Industrial safety

Future efforts will focus on real-world tests and integrating technologies like deep learning for even better performance.

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