SkyVision Platform Enables Efficient Detection of Crossing Boundaries/Tripwires

In the video surveillance industry, the detection of crossing boundaries/tripwires is crucial. WeLinkirt's SkyVision platform provides an efficient solution for this scenario with advanced technology.

User scenario: A large industrial park has a clear division between its production area and office area, and some dangerous areas have strict restrictions on personnel entry. In the daily operation of the park, real-time monitoring of personnel and vehicles in each area is required, especially to detect whether there are any situations where personnel or vehicles cross the boundary or tripwires. The monitoring objects are the personnel and various vehicles in the park.

Pain points: The traditional video surveillance method mainly relies on manual viewing of surveillance footage, which incurs high labor costs and low efficiency. Manual monitoring is prone to fatigue and negligence, resulting in a missed detection rate of up to 15% and a false alarm rate of 12%. In today's trend of emphasizing the use of new AI infrastructure to improve the interaction experience and application expansion, this traditional method can no longer meet the park's needs for safe and efficient management. Moreover, when the area division or monitoring rules of the park change, resetting the monitoring system requires a large amount of time and manpower.

Technical Principle

The SkyVision 0-code video surveillance AI platform uses advanced deep learning algorithms and computer vision technology. In image recognition, it employs Convolutional Neural Networks (CNN) to extract and analyze features from each frame of the surveillance video. Through learning from a large number of samples, the model can accurately identify the outlines and shapes of personnel and vehicles. For boundary/tripwire detection, the platform pre-sets boundaries and tripwires in the surveillance image and uses coordinate positioning technology to instantly determine whether the position of personnel or vehicles exceeds the set range. Once a crossing or tripwire event is detected, the system immediately issues an alarm. This technical principle is effective because deep learning algorithms can continuously learn and optimize to adapt to the monitoring needs of different scenarios and can quickly and accurately process a large amount of video data.

• Convolutional Neural Networks (CNN) for feature extraction to improve recognition accuracy.
• Coordinate positioning technology for real-time position judgment to ensure timely detection of boundary-crossing behaviors.
• Deep learning algorithms can continuously learn and optimize to adapt to different scenarios.

WeLinkirt's Solution and Product

Centered around the SkyVision 0-code video surveillance AI platform, it has the ability to train its own model on - site within hours, and can train a suitable boundary/tripwire detection model according to the actual needs of the park in a short time. Through the behavior/event recognition function, it can accurately identify the boundary-crossing behaviors of personnel and vehicles. At the same time, it is equipped with an edge box to issue real-time alarms. Once an abnormal situation is detected, an alarm is immediately issued. The platform also supports 100% local data storage without leaving the park, ensuring the security of the park's data. In addition, the DaoAI World model provides semantic understanding capabilities, further enhancing the platform's intelligent analysis capabilities. When the monitoring rules of the park need to be adjusted, 0-code operations can be performed on the platform to quickly complete the re-configuration.

The SkyVision platform provides reliable protection for the boundary/tripwire detection in the park with its powerful functions and high-efficiency performance.

Quantitative results: After using the SkyVision platform, the missed detection rate has been reduced from 15% to <0.6%, greatly improving the detection accuracy. The false alarm rate has been reduced from 12% to -95%, effectively reducing unnecessary interference. At the same time, the re-configuration time has been shortened from several hours to 5min, improving the flexibility and response speed of the system.