Multimodal Diffusion Video Anomaly Detection With Feature Restoration
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Solution Overview
Problem
Existing video anomaly detection systems face challenges due to low occurrence frequency of abnormal behaviors, leading to insufficient training data and low accuracy, particularly when relying on fragmentary information from frame images.
Innovation Solution
A method utilizing multimodal diffusion that extracts visual, text, and motion feature vectors from video frames, injects noise into the visual feature vector, and uses a combination of first and second diffusion models with different conditions to remove noise iteratively, enhancing anomaly detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only frame images are used for training AI models, then the system complexity is low, but the detection accuracy is low due to fragmentary information
Solution Approach 1:
The patent combines multiple information sources (frame images, audio signals, text descriptions) into a unified multimodal feature vector representation. This merging of diverse data types enriches the training information beyond single-frame images, improving anomaly detection accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The system processes multiple types of data (visual, auditory, textual) through a unified diffusion model framework that handles diverse input modalities. This multi-functional approach allows the same core architecture to process different information types, improving detection accuracy without proportionally increasing complexity.
2Measurement precision
If sufficient image data is secured for training, then the detection accuracy improves, but the data acquisition time and cost increase due to low occurrence frequency of abnormal behaviors
Solution Approach 1:
The system performs preliminary actions by generating synthetic anomaly data through the diffusion model before actual detection is needed. By pre-generating diverse anomaly scenarios and incorporating them into training data, the system overcomes the limitation of low occurrence frequency without requiring extensive real-world data collection over time.
Solution Approach 2:
The diffusion model acts as an intermediary that bridges the gap between limited real anomaly data and sufficient training data requirements. It generates realistic synthetic anomaly samples that supplement scarce real data, enabling effective training without prolonged data collection periods.
3Measurement precision
If multimodal diffusion models are used for anomaly detection, then the detection accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the complex multimodal processing into distinct stages: feature extraction from different modalities, noise injection, diffusion-based restoration, and anomaly scoring. This segmentation allows each component to be optimized independently and processed efficiently, managing computational complexity while maintaining high detection accuracy.
4Measurement precision
If noise is injected into feature vectors for diffusion processing, then the restoration quality improves, but the processing steps and computational load increase
Solution Approach 1:
The diffusion model employs periodic action through iterative noise injection and restoration cycles. By systematically applying noise at controlled intervals and performing corresponding restoration steps, the system achieves high restoration quality while managing processing time through structured repeated operations rather than continuous processing.
Data Source
AI summary
Proposed are a method of detecting a video anomaly on the basis of multimodal diffusion, and the method includes a step of obtaining video data including a plurality of frames, a step of detecting an object included in each of the plurality of frames, a step of extracting a multimodal feature vector including a visual feature vector, a text feature vector, and a motion feature vector for the detected object, a step of generating a noise vector by injecting noise into the visual feature vector, a step of generating a restoration vector with the noise removed by inputting the noise vector into a diffusion model and by using the text feature vector and the motion feature vector as conditions, and a step of performing anomaly detection on the video data by comparing the visual feature vector and the restoration vector.