Security behavior event identification method and system based on multi-modal analysis
By collecting multimodal monitoring data and performing feature fusion, and using AI models to classify events, the problem of inaccurate security behavior event identification caused by a single data source is solved, and more efficient security behavior identification is achieved.
CN120544129AActive Publication Date: 2025-08-26GUANGXI CHUANGXUAN TECH CO LTD
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Patent Information
- Application Number
- CN202510699438.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-28
- Publication Date
- 2025-08-26
- Estimated Expiration
- 2045-05-28
AI Technical Summary
Technical Problem
Existing security technologies rely on a single data source and are difficult to fully perceive monitoring scenarios, resulting in low accuracy in identifying security behavior events and prone to misjudgment or misjudgment.
Method used
Collect multimodal monitoring data, including visual imaging and environmental sensing data, perform spatial feature extraction and timing feature extraction, generate fusion monitoring feature vectors, and use AI security behavior recognition models to classify events.
Benefits of technology
Through multimodal analysis, the accuracy and comprehensiveness of security behavioral events recognition are improved, and the security of monitoring scenarios is ensured.
✦ Generated by Eureka AI based on patent content.
Abstract
The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a security behavior event recognition method and system based on multi-modal analysis, and the method comprises the steps: collecting a multi-modal monitoring data set of a target monitoring scene, the multi-modal monitoring data set comprising a visual imaging data stream and an environment sensing data stream; performing spatial feature extraction processing on the visual imaging data stream to obtain a motion state feature set of the target object; time sequence feature extraction processing is carried out on the environment sensing data stream to obtain an environment state feature set, and the environment state feature set comprises a sound field distribution feature, a temperature gradient feature and an illumination intensity feature; and carrying out space-time fusion processing on the motion state feature set and the environment state feature set to generate a fusion monitoring feature vector, calling an AI security behavior recognition model to carry out event classification processing on the fusion monitoring feature vector, and outputting a security behavior event recognition result of the target monitoring scene.
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