With the rapid development of AI technology in the field of video surveillance behavior recognition, its application scenarios are constantly expanding. In the perimeter intrusion/over-crossing detection scenario of the emergency security/smart security industry, WeLinkirt's SkyVision platform demonstrates strong advantages.
User Scenario: A large-scale park needs to conduct real-time monitoring of the park perimeter in terms of emergency security to prevent unauthorized personnel from climbing over or intruding. The detection objects are the climbing-over and intrusion behaviors of personnel within the perimeter area, covering the entire perimeter defense line of the park, which is crucial for ensuring the safety of people and property in the park.
Pain Points: In traditional perimeter intrusion detection systems, there are many problems. In terms of quantitative dilemmas, the miss-detection rate is relatively high, reaching about 5%, which means that some intrusion behaviors may not be detected in time. At the same time, false alarms are serious, with a false-alarm rate as high as 30%. A large number of false alarms not only waste the time and energy of security personnel but may also cause real intrusion behaviors to be ignored. In addition, with the development of AI technology in video surveillance behavior recognition, traditional systems cannot quickly adapt to new detection requirements and are difficult to accurately identify complex behaviors, failing to meet the high-standard requirements of current emergency security.
Technical Principle
The SkyVision zero-code video surveillance AI platform uses advanced deep-learning algorithms. Based on the Convolutional Neural Network (CNN), it can extract and analyze the features of targets in video images. By training its own model on - site within hours, the platform can specifically learn the characteristics of perimeter intrusion and climbing-over behaviors. On the one hand, it uses the semantic understanding ability of the DaoAI World model to accurately make semantic judgments on the behaviors of personnel in the scene, such as distinguishing between normal personnel activities and illegal climbing-over behaviors. On the other hand, combined with the real-time processing ability of the edge box, it analyzes and processes video data locally, avoiding the delay and risk of data transmission. This method is effective because CNN can automatically learn the feature patterns of images, the semantic understanding of the world model enables the platform to understand the meaning of behaviors from a more macroscopic level, and the local processing of the edge box ensures the real-time performance and data security of the system.
- The CNN algorithm automatically learns image features to improve the accuracy of target recognition.
- The DaoAI World model conducts semantic understanding to accurately distinguish different behaviors.
- The edge box realizes local real-time processing to reduce data transmission delay.
- Training its own model on - site within hours to meet the needs of specific scenarios.
WeLinkirt's Solution and Product
Centered on the SkyVision zero-code video surveillance AI platform, this platform has the ability to train its own model on - site within hours and can quickly conduct customized training for the specific scenario of perimeter intrusion detection. Through the behavior/event recognition function, it can accurately judge the climbing-over and intrusion behaviors of personnel. At the same time, the real-time alarm function of the edge box can immediately issue an alarm when an abnormal behavior is detected, notifying security personnel to handle it in time. In addition, the platform supports 100% local data storage without leaving the site, ensuring data security. The supporting DaoAI World model provides semantic understanding ability for the platform, enhancing the recognition and judgment of complex behaviors.
The SkyVision platform brings an efficient, accurate and secure solution for perimeter intrusion detection in emergency security.
Quantitative Results: After using the SkyVision platform, the detection rate of perimeter intrusion detection has been greatly increased to 98%, and the miss-detection rate has been reduced to <2%, effectively avoiding potential safety hazards caused by miss-detection. At the same time, the false-alarm rate has been significantly reduced to -80%, greatly reducing the workload of security personnel. In terms of system change-over, due to the zero-code feature of the platform, the change-over time has been shortened to 5min, enabling it to quickly adapt to different detection requirements.