Intelligent monitoring method, system and device for floating population
By analyzing surveillance video data and utilizing trajectory algorithms and outlier detection algorithms, the problem of low efficiency in monitoring the floating population in existing surveillance systems has been solved, enabling intelligent monitoring and early warning of the floating population.
CN117011783BActive Publication Date: 2026-07-03WUHAN FIBERHOME DIGITAL TECH CO LTD
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Patent Information
- Application Number
- CN202310812037.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-07-04
- Publication Date
- 2026-07-03
- Estimated Expiration
- 2043-07-04
AI Technical Summary
Technical Problem
Existing monitoring systems are unable to perform intelligent analysis and cannot efficiently and accurately monitor and warn of the floating population, especially in dynamic monitoring scenarios where their effectiveness is poor.
Method used
By collecting surveillance video data, and based on preset trajectory algorithms and outlier detection algorithms, the system analyzes the personal trajectories and abnormal behaviors of the floating population, and pushes early warning information.
Benefits of technology
It enables dynamic tracking and abnormal behavior analysis of the floating population, allowing for timely and accurate early warnings to prevent potential risks.
✦ Generated by Eureka AI based on patent content.
Abstract
The application relates to the technical field of community security management, and discloses a floating population intelligent monitoring method, system and equipment. The method comprises the following steps: determining floating population data flow according to behavior event information in monitoring image data of a preset monitoring area collected; obtaining personal trajectory data of the floating population data flow by dynamically tracking the floating population data flow based on a preset trajectory algorithm; analyzing the personal trajectory data by a preset outlier detection abnormal behavior algorithm to obtain abnormal behavior data of the floating population data flow; and pushing early warning information when the abnormal behavior data meets an abnormal behavior early warning threshold. In the application, the monitoring video of the area where the floating population is located can be analyzed, the activity trajectory of the floating population can be recorded, the dynamic trajectory can be described, the personal trajectory data can be analyzed and counted for abnormal behavior by the preset outlier detection abnormal behavior algorithm, and early warning can be performed when the early warning threshold is exceeded, so that early warning can be timely and accurately sent out.
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