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The current development of highway safety infrastructure is witnessing the emergence of various intelligent video analytics technologies. These include advanced license plate recognition, vehicle trajectory tracking, traffic flow detection and analysis, and more. By leveraging these innovations, it becomes possible to detect speeding vehicles along roads, measure their speed, and collect traffic statistics in specific areas. It also enables the identification of illegal lane changes by large vehicles, as well as the monitoring of unauthorized use of emergency lanes, random parking, and reverse driving. Additionally, it supports the detection of key highway entrances and exits, illegal passenger boarding, and accident surveillance through video capture.
As technology evolves, the application of video surveillance in highways and high-speed railways has become increasingly sophisticated. For high-speed rail systems, safety monitoring is typically categorized into five main areas: natural disaster monitoring (such as earthquakes, rainfall, and wind conditions), track monitoring (including temperature and roadbed issues), monitoring of large structures (like station buildings, tunnels, and signaling equipment), object intrusion detection, and train operation status monitoring. While traditional systems rely on a variety of sensors, the growing maturity of video surveillance and analytics has led to increased adoption of video-based integrated monitoring systems.
The front-end video surveillance points in active infrared night vision systems are often located in harsh outdoor environments where 24/7 monitoring is required. In low-light or adverse weather conditions, such as rain or fog, the system must maintain visibility over distances of about 1-2 kilometers. There are currently three types of night vision technologies available: passive infrared imaging, active infrared using infrared light, and laser-based active infrared. Passive infrared is suitable for detecting moving objects like people or vehicles at night but cannot monitor the surrounding environment. Active infrared with lamps has a limited range (up to 300 meters) and suffers from short lifespan and bulkiness. Laser-based systems, however, are more mature, consisting of infrared lasers, ultra-low light color cameras, and infrared night vision cameras.
Laser technology offers significant advantages, including longer range, higher brightness, and better directionality. The choice of wavelength can enhance the sensitivity of CCD sensors. Laser lamps have a long lifespan—typically over 10,000 hours. Cameras used in such systems must balance daytime and nighttime performance. During the day, they filter out invisible light to produce realistic colors, while at night, they switch to infrared mode to ensure effective night vision.
Night-vision lenses generally have a smaller F-number than conventional lenses, allowing more light to enter and improving low-light performance. They are also treated with anti-reflective coatings to increase near-infrared transmittance. To capture distant objects clearly, electric telephoto lenses are commonly used.
Intelligent video analytics technology originates from research in computer vision and artificial intelligence. Its goal is to map images to event descriptions, enabling computers to identify and distinguish key targets from video footage. This technology enhances traditional surveillance systems by filtering out irrelevant data, automatically extracting useful information, and transforming cameras into "intelligent" devices capable of learning and analyzing patterns.
This advancement significantly improves the capabilities of video surveillance systems, making them more efficient and reducing the need for human oversight. Intelligent video analytics plays a crucial role in fire alarms, foreign object detection, and monitoring of stations and bridges.
Today, this technology is gaining widespread attention in intelligent road monitoring and is being applied in real-world projects. Currently, there are three main architectures for road video intelligent analysis systems:
1. **Intelligent Analysis Camera**: This is the most common approach, where the analytics algorithm is embedded directly into the camera. When a vehicle is detected, the camera processes the video in real-time and sends the results to the backend. This method is efficient, reduces bandwidth usage, and allows dual-stream output if needed.
2. **Standalone Video Analytics Device**: These are dedicated devices that analyze video streams from front-end cameras. They support multiple HD video channels and offer powerful processing capabilities, making them compatible with various high-definition cameras.
3. **Back-end Processing Method**: Many traffic platforms now include intelligent detection modules that process video streams from front-end cameras. While this approach offers centralized control and strong analytical power, it can place heavy demands on network bandwidth, especially when handling large volumes of data.
In conclusion, the effectiveness of intelligent analysis depends heavily on clear image quality. Whether using front-end or back-end processing, high-definition video is essential for accurate analysis. High-quality images not only improve visual clarity but also provide a more reliable foundation for intelligent decision-making.
**Integrated Monitoring Host**: Traditional matrix systems have separate alarm hosts but often lack flexibility in accessing different protocols. Modern embedded systems based on ARM9 offer greater stability and support for multiple input/output interfaces, including RS-485, CANBUS, and digital signals.
Video surveillance remains a critical component of security systems, offering intuitive, timely, and detailed information. With advancements in computing, networking, and image processing, video surveillance continues to evolve, promising even greater contributions to traffic safety in the future.
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