AI Vision Enables Precise Passenger Flow Statistics and Heat Map Analysis

With the continuous development of technology, video surveillance and intelligent monitoring are playing an increasingly important role in modern society. Among them, passenger flow statistics and heat map analysis are of great significance for the operation and management of commercial venues and public facilities. WeLinkirt has brought new breakthroughs to this field with advanced technology.

In today's social environment, video surveillance and intelligent monitoring have become important means to ensure safety and optimize management. For commercial venues such as large shopping malls and supermarkets, as well as public facilities like airports and railway stations (transportation hubs), passenger flow statistics and heat map analysis are particularly crucial. Take a leading video surveillance manufacturer as an example. It serves many commercial complexes and transportation hubs, and one of its core businesses is to conduct passenger flow statistics and heat map analysis for these places. Accurate passenger flow data can help commercial venues reasonably arrange the number of employees and optimize the store layout, while heat map analysis can reveal customers' behavior patterns and areas of interest, providing a basis for formulating marketing strategies. For transportation hubs, passenger flow statistics and heat maps help to allocate resources reasonably and ensure the safety and smooth travel of passengers.

Pain Points: Why It's Difficult

Traditional methods of passenger flow statistics and heat map analysis face many quantification dilemmas. In terms of the miss rate, manual statistics or simple algorithms can hardly accurately identify all people, and the miss rate can reach about 10%. This is because in real-world scenarios, the flow of people is frequent and complex. There may be situations such as people blocking each other and rapid movement, which makes it impossible for traditional methods to capture every person accurately and in time. For example, during a promotion event in a shopping mall, a large number of customers gather, and the mutual blocking among people makes it easy for traditional methods to miss some customers.

The false alarm problem is also a major pain point of traditional methods. Traditional methods are prone to misjudging non-human objects as people, with a false alarm rate of about 8%. This is because traditional algorithms have limited ability to recognize object features and cannot accurately distinguish people from some objects with similar appearances, such as large dolls and trolleys. False alarms will affect the reliability of the analysis results and lead to deviations in operational management decisions.

In addition, traditional methods require a large amount of manpower for data processing and analysis, which is inefficient and difficult to update data in real-time. Manual data processing is not only slow but also prone to human errors. Moreover, after data collection, it needs to go through a cumbersome process before analysis, which greatly reduces the timeliness of the data. In some scenarios that require real-time decision-making, traditional methods cannot meet the requirements.

Technical Principles

WeLinkirt uses advanced deep learning algorithms and computer vision technology to solve these problems. The deep learning algorithm is trained with a large amount of image data and has strong feature extraction and classification capabilities. It can learn various features of people, including appearance, posture, and movement, so as to accurately identify people's features and behavior patterns. Compared with traditional algorithms, the deep learning algorithm can better adapt to different scenarios and lighting conditions, and can accurately identify people even in complex environments.

Computer vision technology uses the video images collected by cameras for real-time analysis and processing. Specifically, the algorithm will detect and track targets in the image, and determine whether they are people by analyzing the movement trajectories and features of the targets. At the same time, WeLinkirt also uses multi-sensor fusion technology, combining data from infrared, laser and other sensors. A single sensor may be affected by environmental factors, resulting in inaccurate detection. The multi-sensor fusion technology can make up for the deficiencies of a single sensor and improve the overall detection performance. For example, in a dimly lit environment, the infrared sensor can provide more accurate information about people. After fusing with the camera data, it can improve the accuracy and reliability of detection.

Typical Application Scenarios

• Conduct passenger flow statistics at the entrance of a commercial complex. When detecting, it is necessary to consider the entry and exit directions, speeds of people and possible congestion. The difficulty lies in the high probability of miss-detection and false-detection when people are dense, and different entrance layouts and lighting conditions will also affect the detection results.
• Conduct heat map analysis of store areas in a shopping mall. It is necessary to accurately identify the distribution of customers in the store, including the stay time and movement trajectories of customers. The difficulty is that the display of goods and the blocking of people in the store will affect the field of view of the camera, resulting in inaccurate data collection.
• Conduct passenger flow statistics and heat map analysis in the waiting area of a transportation hub. It is necessary to consider the luggage-carrying situation of passengers, different walking speeds and the gathering and dispersing of the crowd. The difficulty lies in the complex behavior patterns of passengers and the large environmental noise, which will interfere with the data collection of sensors.
• Conduct passenger flow statistics at the elevator entrance of public facilities. It is necessary to detect the entry and exit of people from the elevator, including the time and number of entries and exits. The difficulty is that the opening and closing of the elevator door will affect the shooting angle of the camera, and the short stay time of people at the elevator entrance increases the difficulty of detection.

Implementation Case

A leading video surveillance manufacturer serves many commercial complexes and transportation hubs. Before adopting WeLinkirt's solution, it faced the problems of high miss rate, high false alarm rate and large manpower input. The manufacturer deployed WeLinkirt's Tianyan SkyVision zero-code video surveillance AI platform in multiple places. During the implementation process, WeLinkirt's technical team trained its own models on - site within hours according to the characteristics of different places to ensure that the models could quickly adapt to different scenarios. Before the launch, the miss rate of passenger flow statistics of the manufacturer was about 10%, the false alarm rate was about 8%, and a large amount of manpower was required for data processing and analysis. After the launch, the miss rate was reduced to <1%, the false alarm rate was reduced by -75%, and the manpower input was reduced by about 80%.

WeLinkirt's Tianyan SkyVision platform brings an efficient and accurate solution to passenger flow statistics and heat map analysis, significantly improving the operational management level.

WeLinkirt's Solution and Products

WeLinkirt provides the Tianyan SkyVision zero-code video surveillance AI platform. The platform has the ability to train its own models on - site within hours and can quickly train suitable models according to different scenarios and needs. In the scenario of passenger flow statistics and heat map analysis, the platform can collect video data in real-time, use the trained models for person detection and tracking, and generate accurate passenger flow statistics data and heat maps. At the same time, the platform supports SDK / API / Docker deployment, can achieve 100% local privatization, ensure that the data does not leave the factory, and guarantee data security.

Quantitative Results

By using WeLinkirt's Tianyan SkyVision platform, the miss rate of passenger flow statistics of the leading video surveillance manufacturer was reduced to <1%, the false alarm rate was reduced by -75%, greatly improving the accuracy and reliability of the data. At the same time, the automated analysis function of the platform reduced the manpower input by about 80%, improved the work efficiency, realized real-time data update, and provided strong support for the operation and management of the places.

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.