SkyVision Platform Enables Accurate Passenger Flow Statistics and Heat Map Analysis

With the expansion of AI technology application in chip design, improving the performance of intelligent security video surveillance equipment has become a hot topic. The SkyVision zero-code video surveillance AI platform demonstrates strong advantages in the scenario of passenger flow statistics and heat map analysis.

User Scenario: A large commercial complex needs to conduct real-time statistics on the passenger flow in different areas during daily operations and generate heat maps to reasonably allocate operational resources and optimize the store layout. A large number of video surveillance devices are installed at the entrances, passages, and store doors of the mall, and the detection objects are the people entering, exiting, and moving inside the mall.

Pain Points: Traditional methods of passenger flow statistics and heat map generation have many problems. On the one hand, due to the limited performance of the chips, the processing capacity of the video surveillance devices is insufficient, resulting in a missed detection rate as high as 15%. As a result, a lot of passenger flow data cannot be accurately counted. On the other hand, false alarms occur frequently, with a false alarm rate of 20%, which interferes with operational decisions. In addition, the labor cost is high, and special personnel are required for data collection and analysis. Combining with the current hot topic of AI technology application in chip design, how to use chip technology to improve the performance of video surveillance devices and solve these pain points has become the key.

Technical Principle

The SkyVision zero-code video surveillance AI platform adopts advanced deep learning algorithms combined with high-performance chip technology. The chips have powerful computing capabilities and can quickly process a large amount of video data. At the algorithm level, the Convolutional Neural Network (CNN) is used to identify and track human features in the video. CNN can automatically extract features such as the shape and contour of the human body and convert them into digital feature vectors, thereby achieving accurate population statistics. At the same time, the spatio-temporal analysis algorithm is used to analyze the passenger flow data at different time periods and spatial positions to generate accurate heat maps. The combination of this algorithm and the chip effectively improves the data processing speed and accuracy.

• The deep learning algorithm can continuously learn and optimize to meet the needs of passenger flow statistics in different scenarios.
• The high-performance chip provides powerful computing support to ensure real-time processing of a large amount of video data.
• The spatio-temporal analysis algorithm takes into account time and space factors, making the heat map more accurately reflect the passenger flow distribution.

WeLinkirt Solution and Product

The core product, the SkyVision zero-code video surveillance AI platform, has the ability to train its own models on - site within hours. Without professional programming knowledge, mall operators can quickly train models for passenger flow statistics and heat map generation suitable for the mall scenario according to their own needs. The platform supports behavior/event recognition and can accurately identify behaviors such as people entering, exiting, and staying. Real - time alarms are sent through the edge box. Once there is an abnormal passenger flow situation, such as a sudden large increase or decrease in the passenger flow in a certain area, an alarm can be issued in time. Moreover, the platform realizes 100% local data storage without leaving the site, ensuring data security. At the same time, combined with the semantic understanding ability of the DaoAI World model, in - depth analysis of passenger flow data is carried out to mine potential operational information.

The SkyVision platform provides strong support for mall passenger flow monitoring with its efficient model training and accurate data processing.

Quantitative Results: By using the SkyVision platform, the missed detection rate is reduced to <2%, greatly improving the accuracy of passenger flow data. The false alarm rate is reduced by -85%, reducing the interference with operational decisions. At the same time, labor costs are saved. The data collection and analysis work that originally required 5 people now only requires 1 person, and the labor cost is reduced by -80%.