Method for detecting abnormal behavior of figure in video based on emotion enhancement

By fusing emotional features and optical flow features in video anomaly detection and combining temporal and spatial attention, the problems of insufficient data and neglect of emotional factors in existing methods are solved, and efficient detection of abnormal behaviors in complex scenes is achieved.

CN120599698APending Publication Date: 2025-09-05TIANJIN UNIVERSITY OF TECHNOLOGY
0 Cites 0 Cited by

Patent Information

Application Number
CN202510723568.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-31
Publication Date
2025-09-05

AI Technical Summary

Technical Problem

Existing video anomaly detection methods lack sufficient anomaly training data when facing complex scenes, rely on limited labeled data, cannot effectively capture complex spatiotemporal information and emotional changes, and ignore the emotional factors behind the behavior, resulting in insufficient detection accuracy.

Method used

By extracting the emotional features and optical flow features of the characters, a cross-modal interaction strategy is adopted to fuse RGB features, optical flow features and emotional features, combined with temporal and spatial attention, and the cross-modal interaction strategy and ranking loss function are used to optimize the model to generate anomaly scores.

Benefits of technology

The accuracy of anomaly detection is improved, and it can accurately identify different types of abnormal behaviors in complex video scenes, enhancing the semantic interpretation and detection capabilities of abnormal behaviors.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a video figure abnormal behavior detection method based on emotion enhancement, which innovatively introduces an emotion feature as a core supplement, and provides deep semantic interpretation for abnormal detection by analyzing an emotion motivation (such as anger, panic and attack tendency) behind a behavior. The emotion features provide high-level semantic constraints for the motion features (flow features) and the appearance features (RGB features), and the motion features and the appearance features provide direct and morphological abnormal clues; through the multi-level feature information fusion, the video anomaly detection return method based on emotion enhancement can make accurate judgment for different types of abnormal behaviors (such as irregular actions, emergencies, abnormal crowd behaviors and the like) in a complex video scene, so that the overall anomaly detection performance is effectively improved.
Need to check novelty before this filing date? Find Prior Art