Method and device for identifying bad behaviors of tourists in tourist attraction
By employing multi-scale image patch segmentation and multi-granularity joint motion data fusion, the problem of insufficient accuracy in human behavior recognition in complex scenic environments was solved, enabling more accurate monitoring and early warning of tourists' undesirable behaviors.
CN122454632APending Publication Date: 2026-07-24LESHAN NORMAL UNIV
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Patent Information
- Application Number
- CN202610585925.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-29
- Publication Date
- 2026-07-24
AI Technical Summary
Technical Problem
Existing video-based human behavior recognition methods struggle to simultaneously account for changes in human movement scale and pose diversity in complex scenic environments, resulting in insufficient recognition accuracy.
Method used
Multi-scale image patch segmentation and downsampling, multi-granularity joint motion data extraction, and cross-modal attention mechanism fusion are employed to generate multi-modal fusion features for behavior recognition.
Benefits of technology
It improves the accuracy of identifying undesirable behaviors in complex scenic environments, enabling more precise and reliable monitoring and early warning of tourist behavior.
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Abstract
The invention discloses a method and a device for identifying bad behaviors of tourists in a tourist attraction, and relates to the technical field of human body behavior action identification. The method comprises the following steps: collecting real-time behavior video data of tourists in a key area of a scenic spot; preprocessing the video data, and respectively extracting spatio-temporal features based on images and motion features based on human body postures; wherein spatio-temporal feature modeling is carried out on video image data by adopting a self-attention mechanism, and motion feature extraction is carried out on a human joint and skeleton sequence by adopting a model based on a graph neural network; and fusing the multi-modal features, and classifying tourist behaviors based on the fused features to identify whether preset bad behaviors exist or not. According to the invention, a multi-modal attention mechanism is introduced to carry out joint modeling on the video spatial-temporal features and the human body posture features, so that the accuracy and stability of tourist bad behavior identification in a complex scene are improved, and automatic and intelligent supervision of tourist behaviors in a scenic spot can be realized.
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