Temporal Filtering for Real-Time Class Transition Classification
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Solution Overview
Problem
Existing multiclass classifiers suffer from decreased accuracy during class transitions due to failure in adapting the classification process to account for possible class transitions, leading to incorrect classifications and inefficiencies in confusion analysis.
Innovation Solution
Implementing temporal filters that leverage classifications from preceding input signals and utilize a confusion matrix to adjust confidence scores, improving classification accuracy by accounting for probable errors and class shifts, while minimizing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a constant signal analysis rate is used, then the classification process is simple and efficient, but accuracy decreases during class transitions
Solution Approach 1:
The patent applies dynamics by transitioning from a constant analysis rate to an adaptive analysis rate that changes based on detected class transitions. When a class transition is detected, the system switches to a different analysis rate (e.g., lower rate during transition, higher rate during stable states), allowing the system to adapt its behavior dynamically to improve accuracy without permanently sacrificing efficiency.
Solution Approach 2:
The system changes the parameter of analysis rate based on the classification state. By monitoring confidence scores and detecting transitions, the system modifies the analysis rate parameter in real-time, using higher rates during stable classifications and adjusted rates during transitions, thus optimizing both accuracy and efficiency under different operating conditions.
2Ease of operation
If the highest confidence score is used for classification, then the process is simple and fast, but classification errors occur during transitions
Solution Approach 1:
The system implements feedback by continuously monitoring confidence scores and using this information to detect class transitions. When a transition is detected (e.g., confidence score dropping below a threshold or switching between classes), the feedback mechanism triggers a change in analysis rate and re-evaluation, preventing incorrect classifications while maintaining simplicity during normal operation.
Solution Approach 2:
The system performs preliminary detection of class transitions by analyzing confidence score patterns before final classification is made. This preliminary action allows the system to prepare for potential misclassification by adjusting the analysis rate in advance, ensuring more reliable classification without adding significant complexity to the overall process.
3Measurement precision
If retraining classifiers is performed to reduce confusion, then classification accuracy improves, but the system complexity and deployment time increase
Solution Approach 1:
The system performs preliminary confusion analysis during the training phase to identify common misclassification patterns and class transition issues. This preliminary action allows the system to pre-configure adaptive analysis rates and transition detection thresholds, so that when the system is deployed, it can handle confusion situations without requiring complex retraining or model adjustments, thus reducing operational complexity.
Solution Approach 2:
The system uses self-service by automatically detecting class transitions and adjusting its own analysis rate based on real-time confidence score patterns. This self-adjusting mechanism eliminates the need for external retraining or manual intervention to handle confusion, allowing the system to maintain high accuracy while keeping the deployment process simple and the operational model straightforward.
4Measurement precision
If temporal filtering is applied to improve accuracy, then classification errors are reduced, but processing latency increases
Solution Approach 1:
The system applies dynamics to the temporal filtering process by using an adaptive analysis rate that changes based on the classification state. During stable states, a higher analysis rate is used to minimize latency, while during transitions, the rate is adjusted to improve accuracy. This dynamic adjustment allows the system to balance between filtering effectiveness and processing speed in real-time.
Solution Approach 2:
The system changes the parameter of analysis rate dynamically based on detected transitions and confidence scores. By adjusting this parameter in real-time, the system can apply temporal filtering intensively when needed (during transitions) while maintaining faster processing during stable states, thus optimizing the trade-off between accuracy improvement and latency minimization without requiring a fixed, always-intensive filtering approach.
Data Source
AI summary
In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals-such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and/or a statistical filter.