Video Stream Anomaly Detection Using Matrix Profile Time Series
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Solution Overview
Problem
Existing analytics cannot be directly applied to video stream data, limiting the ability to detect and respond to anomalies in real-time video analysis.
Innovation Solution
Processing video frames to generate feature time series, creating a matrix profile to identify similarities and anomalies, and performing automatic actions when anomalies exceed a threshold value, with the option to predict anomalies by identifying precursor patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video streams are processed to generate time series data for anomaly detection, then the ability to detect and respond to anomalies is improved, but the complexity of the system increases due to the need for additional processing steps
Solution Approach 1:
The patent introduces an intermediary processing layer that converts video stream data into time series data, which can then be analyzed using matrix profile algorithms. This intermediary representation enables the use of sophisticated anomaly detection techniques while maintaining a clear separation between video processing and analysis functions, thereby managing system complexity.
Solution Approach 2:
The system segments the video analysis process into distinct stages: frame processing to extract features, conversion to time series data, matrix profile generation, and anomaly detection. This segmentation allows each component to be optimized independently and facilitates modular implementation, reducing overall system complexity.
2Measurement precision
If matrix profile algorithms are used to identify anomalies in time series data, then measurement precision is improved, but the computational time and processing speed increase
Solution Approach 1:
The system applies matrix profile algorithms to identify anomalies by comparing subsequences of time series data. While this provides high measurement precision for anomaly detection, the computational complexity increases with longer time series and larger window sizes, resulting in increased processing time.
Solution Approach 2:
The patent adjusts parameters such as the window size and threshold values in the matrix profile algorithm to balance between detection precision and processing speed. By optimizing these parameters, the system achieves acceptable anomaly detection accuracy while reducing computational overhead.
3Productivity
If automatic actions are implemented in response to detected anomalies, then operational efficiency is improved, but the risk of false alarms and incorrect responses increases
Solution Approach 1:
The system implements automatic actions based on anomaly detection results, but the effectiveness depends on the accuracy of the matrix profile-based detection. False alarms can trigger incorrect automatic responses, creating a feedback loop that may compound errors. The system needs robust validation mechanisms to ensure reliable automatic responses.
Data Source
AI summary
Methods and systems for detecting and predicting anomalies include processing frames of a video stream to determine values of a feature corresponding to each frame. A feature time series is generated that corresponds to values of the identified feature over time. A matrix profile is generated that identifies similarities of sub-sequences of the time series to other sub-sequences of the feature time series. An anomaly is detected by determining that a value of the matrix profile exceeds a threshold value. An automatic action is performed responsive to the detected anomaly.