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In today’s rapidly evolving infrastructure, highway safety is increasingly relying on advanced intelligent video analytics technologies. These innovations include new license plate recognition systems, vehicle trajectory tracking, traffic flow analysis, and more. By leveraging these technologies, it becomes possible to detect speeding vehicles along the route, measure their speed, analyze traffic patterns in specific areas, and identify illegal lane changes by large vehicles. Additionally, they can capture incidents such as unauthorized use of emergency lanes, random parking, and reverse driving. These systems also support monitoring key highway entrances and exits, detecting illegal passengers, and recording accident footage. As video surveillance technology matures, its application in highways and high-speed rail systems has become more widespread and efficient.
For high-speed railways, safety monitoring involves five main categories: natural disaster monitoring (such as earthquakes, rainfall, floods, and wind conditions), line monitoring (like track temperature and roadbed issues), monitoring of large structures (including station buildings, tunnels, and signal systems), object intrusion detection, and train operation status. Traditional systems rely on various sensors, but with advancements in video analytics, integrated video-based monitoring systems are now gaining popularity.
The front-end surveillance points of active infrared night vision systems are typically located outdoors in harsh environments where all-weather visibility is essential. At night, there is no ambient light, and during rainy or foggy conditions, maintaining a clear visual distance of about 1–2 kilometers is crucial. Current night vision technologies include passive infrared imaging, active infrared using lamps, and laser-based active infrared. While passive systems are good for detecting moving objects at night, they cannot monitor the environment effectively. Infrared lamp-based systems have limited range (up to 300 meters) and shorter lifespans, making them less suitable for outdoor use. Laser-based systems, however, are more mature and offer better performance, including long-range visibility, high brightness, and durability.
Laser technology provides strong light intensity and excellent directionality, which enhances the camera's sensitivity to specific wavelengths. These systems often include infrared laser lights, ultra-low-light color cameras, and night vision cameras. The lenses used in night vision systems are designed with smaller F-numbers, allowing more light to enter and improving low-light performance. They also feature anti-reflective coatings for near-infrared light and are often paired with telephoto lenses for long-distance viewing.
Intelligent video analytics technology originates from computer vision and artificial intelligence research. Its goal is to map images to event descriptions, enabling computers to identify and distinguish important objects from video streams. This allows video surveillance systems to filter out irrelevant data, automatically extract useful information, and make cameras "intelligent" — transforming them into smart systems that can think and learn.
This shift significantly enhances the capabilities of video surveillance, reducing the need for human operators while increasing efficiency and accuracy. Intelligent video analytics plays a vital role in fire detection, foreign object intrusion alerts, and monitoring of stations and bridges.
Currently, there are three main approaches to implementing intelligent video analytics in road monitoring:
1. **Intelligent Analysis Camera**: This is the most common method, where the algorithm is embedded directly in the camera. When motion is detected, the camera processes the video locally and sends the results to a central system. This reduces bandwidth usage and improves processing speed.
2. **Standalone Video Analytics Device**: These devices are dedicated to analyzing video streams from front-end cameras. They can process multiple HD video channels and support both image and video output. Their powerful processing capabilities make them ideal for integration with high-definition cameras.
3. **Back-end Processing Method**: Many traffic platforms now include built-in analytics modules that process video streams from front-end cameras. This centralized approach offers flexibility and strong analytical power, though it requires significant network bandwidth and may impact system performance when handling large volumes of data.
In conclusion, the success of intelligent video analytics depends heavily on clear and high-quality images. Whether implemented at the front-end or back-end, high-definition video is essential for accurate analysis. It not only improves visual quality but also provides a more reliable foundation for intelligent systems.
Integrated monitoring hosts are also evolving. Traditional systems had limited alarm capabilities, but modern embedded systems based on ARM9 technology offer greater stability and multi-channel support. These hosts can handle various bus protocols like RS-485, CANBUS, and RS-232, making them ideal for complex monitoring environments.
Overall, video surveillance remains a critical component of security systems. With ongoing advancements in computing, networking, and image processing, video surveillance technology continues to evolve, offering improved safety and reliability in transportation and beyond.
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