SkyVision: Local Deployment Ensures Data Security for Passenger Flow Monitoring

The Tianyan SkyVision 0-code video surveillance AI platform of WeLinkirt (DaoAI) (on-site training of self-owned models within hours, behavior/event recognition, real-time alerts from edge boxes, 100% local data storage without leaving the site, and semantic understanding of the DaoAI World model) has improved the accuracy of passenger flow statistics from 90% to 98% through local private deployment. In today's video surveillance/intelligent monitoring industry, accurate passenger flow statistics and heat map analysis are crucial for business operations and security management. Commercial places such as shopping malls and supermarkets need to understand the real-time passenger flow situation to arrange human resources reasonably and optimize the layout. However, traditional passenger flow statistics and monitoring methods have many deficiencies in data security and accuracy.

Industry background and user scenario: In the scenario of passenger flow statistics and heat map in the video surveillance industry, the Tianyan SkyVision 0-code video surveillance AI platform of WeLinkirt solves the core problems of data security and statistical accuracy. As mentioned before, it has increased the accuracy of passenger flow statistics from 90% to 98%. Currently, the video surveillance industry is widely used in various fields. For commercial places, accurate passenger flow statistics can help managers understand customers' behavior patterns and preferences. For example, shopping malls can adjust the store layout and promotional activities reasonably according to the passenger flow heat map. Traditional passenger flow statistics methods, such as manual counting and infrared sensing, have problems such as low efficiency and inaccurate data. Moreover, with the improvement of data security awareness, how to ensure the non-disclosure of passenger flow data has become the focus of customers' concern.

Pain points: Why is this hurdle so difficult to cross?

Multi-dimensional quantification of pain points: In traditional passenger flow statistics and monitoring, the missed detection rate is relatively high, reaching an average of 10%. This means that a large amount of passenger flow data may be missed, affecting subsequent analysis and decision-making. The false alarm rate is also not negligible, about 8%, which will cause managers to receive a large number of invalid messages and waste their energy. The manual re-judgment time is relatively long. It takes about 30 minutes of manual re-judgment for every hour of video, increasing labor costs. In terms of compliance risks, since traditional solutions are difficult to guarantee the secure storage and transmission of data, they may face the risk of data leakage and violate relevant regulations. In terms of unit cost, each additional monitoring point requires a relatively high investment in equipment and maintenance costs.

Root cause analysis: From the process level, the traditional passenger flow statistics equipment and algorithms are relatively backward and cannot accurately identify the passenger flow situation in complex environments, such as scenes with dense crowds and rapid movement. In terms of imaging, the resolution and frame rate of some surveillance cameras are relatively low, resulting in blurred images and affecting the accuracy of passenger flow statistics. In terms of materials, the stability and durability of some equipment are relatively poor, and they are prone to failure. From the rhythm level, traditional solutions cannot process a large amount of passenger flow data in real time, resulting in untimely data updates. With China Telecom's entry into the security equipment manufacturing field, the market competition has intensified, and customers' requirements for data security and statistical accuracy are getting higher and higher, making the deficiencies of traditional solutions more prominent.

Technical principle

In - depth mechanism: The Tianyan SkyVision 0-code video surveillance AI platform of WeLinkirt uses advanced deep learning algorithms. By learning and training a large amount of passenger flow data, it can accurately identify the passenger flow situation in different scenarios. The platform uses the DaoAI World model for semantic understanding and can accurately analyze the behavior and events of passenger flow. In terms of hardware, it is equipped with high-performance edge boxes that can process video data in real time and achieve real-time alerts. At the same time, the platform supports 100% local private deployment, and the data does not leave the site, ensuring data security.

Comparison with traditional methods: Compared with traditional rule-based AOI and manual visual inspection methods, SkyVision has significant advantages. The traditional rule-based AOI method requires pre-setting complex rules and has poor adaptability to complex scenarios, while SkyVision can automatically adapt to different scenarios through deep learning, improving the accuracy of statistics. Manual visual inspection is not only inefficient but also prone to fatigue and misjudgment. SkyVision can achieve 24-hour uninterrupted monitoring, greatly improving work efficiency. In actual tests, the accuracy of passenger flow statistics of traditional methods is about 90%, while SkyVision can increase it to 98%.

Typical application scenarios

• Shopping mall entrance: By installing surveillance cameras at the entrance of the shopping mall, SkyVision can count the number of passengers entering the mall in real time. The difficulty lies in the frequent and fast entry and exit of people at the entrance, and there may be situations of people blocking each other. Through advanced algorithms and high-frame - rate monitoring, the platform can accurately identify the entry and exit of each person.
• Store entrance: Count the passenger flow at the entrance of each store to help merchants understand the attractiveness of the store. The difficulty lies in the fact that there may be multiple directions of pedestrian flow at the store entrance and the intersection of pedestrian flow with the public area of the mall. SkyVision can distinguish and count the pedestrian flow in different directions.
• Promotion activity area: In the promotion activity area, the crowd is more concentrated. Accurately counting the passenger flow is crucial for evaluating the effect of the activity. The difficulty lies in the individual identification in a dense crowd. The platform uses deep learning algorithms to accurately identify each person in a complex crowd and achieve precise passenger flow statistics.
• Parking lot: Count the incoming and outgoing vehicles and people in the parking lot to understand the usage of the parking lot. The difficulty lies in the complex light environment in the parking lot and the fast movement speed of vehicles and people. SkyVision can adapt to different light environments and accurately identify the entry and exit of vehicles and people.

Implementation case

Comparison before and after the implementation of an anonymous customer: A large-scale commercial complex introduced the Tianyan SkyVision 0-code video surveillance AI platform of WeLinkirt. Before the implementation, the commercial complex used traditional passenger flow statistics methods, with a missed detection rate of 10%, a false alarm rate of 8%, and a long manual re-judgment time. It took 30 minutes of manual re-judgment for every hour of video. After the implementation, the missed detection rate was reduced to 2%, the false alarm rate was reduced to 1%, and the manual re-judgment time was almost zero. At the same time, due to the 100% local private deployment, the security of passenger flow data was guaranteed, avoiding the risk of data leakage.

The Tianyan SkyVision of WeLinkirt brings an efficient, accurate and secure solution to the passenger flow statistics and monitoring of commercial places.

WeLinkirt's solution and product

Product capabilities and implementation methods: The Tianyan SkyVision 0-code video surveillance AI platform of WeLinkirt has powerful capabilities. In terms of modeling, it supports on - site training of self-owned models within hours, and users can quickly train models suitable for their own scenarios according to actual needs. When changing scenarios, there is no need for complex programming and settings, and it can quickly adapt to different monitoring scenarios. In terms of deployment, it supports 100% local private deployment, and the data does not leave the site, ensuring data security. In terms of integration, the platform can be seamlessly integrated with other security systems and business management systems. At the same time, the supporting DaoAI World model can provide more in - depth semantic understanding and cross-scenario generalization capabilities.

Quantified results and business value: By using the Tianyan SkyVision of WeLinkirt, the accuracy of passenger flow statistics in a commercial complex has been increased to 98%, the missed detection rate has been reduced to 2%, the false alarm rate has been reduced to 1%, and the manual re-judgment time is almost zero. This not only improves the accuracy and timeliness of data, providing more reliable decision-making basis for managers, but also reduces labor costs and the risk of data leakage. At the same time, through accurate passenger flow statistics and heat map analysis, the commercial complex can optimize the store layout, adjust promotional activities, and improve overall operational efficiency and profitability.

This article was generated by AI. Customer cases are simulated scenarios based on real product capabilities and figures are illustrative; see product pages for official benchmarks.