Adaptive cameras for smart city monitoring
Researchers in South Korea have developed a smart city traffic monitoring system using adaptive cameras.
The system dynamically activates more cameras during busy times and fewer during quiet periods, optimizing the use of resources and improving road safety. The system was tested in a range of situations and shows the potential to reduce accidents, ease congestion and conserve energy.
With the rise of autonomous vehicles and connected transportation systems, dynamic surveillance solutions are critical to ensuring smooth traffic flow, minimizing accidents, and optimizing efficiency. However, traditional static camera setups often fail to adapt to rapid changes in traffic conditions, resulting in inefficient monitoring and resource use.
To address this issue, researchers from Incheon National University, led by Associate Professor Hyunbum Kim, have introduced an augmented fluid surveillance system that adapts in real-time to varying traffic scenarios.
The system uses a network of single-lens cameras arranged in a grid. This adjusts its surveillance coverage by activating or deactivating cameras based on real-time traffic conditions, ensuring efficient and flexible monitoring.
“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 revolutionize traffic management and provide seamless intelligent transportation services,” said Prof Kim.
To achieve this, the 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, organizes 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 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.
Simulations showed these methods worked effectively under different conditions, such as varying traffic levels. The system reduced energy use during low traffic and provided strong coverage during peak hours by predicting and adjusting to traffic patterns.
“Our approach optimizes camera usage and saves energy while ensuring reliable surveillance. It’s a step toward smarter and more eco-friendly traffic management,” highlights Dr. Kim.
Beyond traffic control, this adaptive system could also be used for crowd monitoring, disaster response, and industrial safety. Future efforts will focus on real-world tests and integrating AI to improve the performance.
Augmented Fluid Surveillance Using Grid Sensing for Intelligent Transportation Service
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