Video Anomaly Detection via Human-Machine Feedback Loop
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Solution Overview
Problem
Current video anomaly detection methods require numerous samples for training, are prone to false alarms, and need frequent model adjustments to maintain accuracy, making them time-consuming and labor-intensive, especially for real-time tasks and identifying easily recognizable objects.
Innovation Solution
A video anomaly detection method based on human-machine cooperation, utilizing an auto-encoder model with Euclidean loss and L2 regularization, where video frames are analyzed, grouped into training and testing sets, and human feedback is integrated to adjust model parameters, improving detection accuracy without updating the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for video anomaly detection, then detection accuracy is improved, but the number of samples needed for training increases significantly
Solution Approach 1:
The patent implements a feedback mechanism where human experts review and correct detection results, and these corrected results are fed back into the system to continuously improve the model's performance without requiring retraining from scratch. This allows the system to learn from mistakes and improve accuracy over time with minimal additional training data.
Solution Approach 2:
The system performs self-optimization by automatically identifying false alarms and missed detections, then using these insights to adjust detection thresholds and parameters. This self-service capability allows the model to improve its own performance without external intervention or complete retraining, reducing the need for large annotated datasets.
2Measurement precision
If detection models are constantly adjusted to improve accuracy, then detection performance improves, but the time and labor required for model optimization increases
Solution Approach 1:
The patent employs dynamic adjustment mechanisms where detection parameters and thresholds are automatically modified based on real-time performance metrics and feedback from human reviewers. This allows the system to adapt to changing conditions and improve accuracy without requiring manual model retraining or extensive optimization time.
Solution Approach 2:
Human experts provide feedback on detection results, and this feedback is systematically used to adjust model parameters and detection thresholds. This feedback loop enables continuous improvement of detection accuracy without requiring time-consuming model retraining, as the system learns from actual performance data rather than requiring extensive re-education of the model.
3Measurement precision
If human experts manually review all detection results, then detection accuracy improves, but the operational complexity and labor requirements increase
Solution Approach 1:
Instead of requiring human experts to review all detection results, the patent implements a selective review mechanism where only a subset of results (particularly uncertain or high-risk detections) are subjected to human review. This partial action approach maintains improvement in detection accuracy while significantly reducing the operational burden and complexity compared to full manual review.
Solution Approach 2:
The system introduces an intermediary layer of automated filtering and prioritization that determines which detection results require human review. This intermediary mechanism sorts detection results based on confidence scores and risk assessments, allowing human experts to focus only on the most critical cases, thereby reducing operational complexity while maintaining high detection accuracy.
4Speed
If the system processes video data in real-time, then detection speed improves, but the computational resources and processing complexity increase
Solution Approach 1:
The patent segments the video processing task into multiple stages: preliminary automated detection, selective human review, and feedback-based optimization. This segmentation allows the system to process video data efficiently in real-time through automated methods while reserving complex human review for only the necessary cases, thereby maintaining high detection speed without proportionally increasing overall processing complexity.
Data Source
AI summary
The disclosure relates to a video anomaly detection method based on human-machine cooperation, in which video frames and traditional descriptors of optical stream of an image are utilized as an input for auto-encoder neural network coding, and converted into a representation content of a hidden layer, and then the representation content of the hidden layer is decoded, reconstructed and output. The auto-encoder network is trained with normal samples. In a test stage, if an input is a normal sample, a final reconstructed error keeps high similarity with an input sample; on the contrary, if the input is an abnormal sample, the final reconstructed error deviates greatly from the input sample.