과적 의심차량 추적에 관한 방법 연구
A Study on the Method of Tracking Suspected Vehicles with Overload
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초록
In contemporary society, traffic concerns and road safety are of paramount importance. Overloading and improper loading significantly jeopardize road safety. Leveraging AI Hub's CCTV traffic videos, overloading hazard data, and Yeosu Sunsin Bridge HS-WIM data from Jeollanam-do, we've crafted detection models. These models facilitate the development of a vehicle detection system calibrated for specific scenarios, making the detection of overloading and loading discrepancies more accessible. Such a system promises to streamline detection operations while substituting manual efforts with AI, thereby economizing on both time and resources.
Abstract
In contemporary society, traffic concerns and road safety are of paramount importance. Overloading and improper loading significantly jeopardize road safety. Leveraging AI Hub's CCTV traffic videos, overloading hazard data, and Yeosu Sunsin Bridge HS-WIM data from Jeollanam-do, we've crafted detection models. These models facilitate the development of a vehicle detection system calibrated for specific scenarios, making the detection of overloading and loading discrepancies more accessible. Such a system promises to streamline detection operations while substituting manual efforts with AI, thereby economizing on both time and resources.
참고문헌 (2)
- Jubi Hwang, "Multi-Target Multi-Camera Vehicle Tracking Systemfor City-Scale Traffic Management", The Institute of Electronics and Information Engineers, pp.797~801 (2022)
- Jinha Song, "Design of Scalable CNN- based Real- time Analysis System for Multiple CCTV Videos Analysis", Korean Institute of Information Scientists and Engineers, pp.962~964 (2021)