Cascade Detection System Early Termination Latency
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Solution Overview
Problem
Conventional cascade-based detection systems incur high operational costs and latency due to processing of numerous negative samples, which outweigh positive examples by several orders of magnitude, making them inefficient in time-sensitive applications where quick response is crucial.
Innovation Solution
Implementing a cascade-based detection system that allows detection of a target at any stage by using detection and rejection thresholds, enabling early termination of processing for negative samples and optimizing the sequence of stages to reduce operational cost and latency, while maintaining or improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cascade-based detection systems process all samples through every stage to ensure maximum accuracy, then detection accuracy is improved, but latency and operational cost increase significantly
Solution Approach 1:
The system dynamically adjusts the detection process by allowing early termination at any stage when confidence exceeds a threshold, rather than following a fixed path through all stages. This dynamic adaptation reduces latency for high-confidence detections while maintaining accuracy for ambiguous cases.
Solution Approach 2:
The system changes the operational parameters of the cascade detector by introducing stage-specific confidence thresholds and enabling detection at intermediate stages. This parameter modification allows the system to balance between speed and accuracy based on confidence levels at each processing stage.
2Reliability
If conventional cascade-based detection systems process all samples through every stage, then detection reliability is improved, but operational cost increases due to processing numerous negative samples
Solution Approach 1:
The system applies partial processing by allowing detection to occur at any stage rather than requiring complete processing through all stages. This partial action approach processes only the necessary number of stages for each sample, reducing operational cost while maintaining sufficient reliability through confidence thresholding.
Solution Approach 2:
The detection process is segmented into independent stages with individual confidence thresholds, allowing the system to evaluate and terminate processing at any segment. This segmentation enables efficient handling of negative samples by stopping processing early when confidence is sufficient, reducing overall operational cost.
3Measurement precision
If the cascade detector is optimized for maximum accuracy by processing all stages, then detection precision is improved, but responsiveness to user input deteriorates
Solution Approach 1:
The system dynamically terminates processing based on confidence thresholds at each stage, adapting the processing depth to the clarity of the detection signal. This dynamic approach improves responsiveness for clear cases while maintaining precision for ambiguous cases through continued processing.
Solution Approach 2:
The system skips remaining stages when confidence exceeds the threshold at any intermediate stage, rushing through the detection process for high-confidence cases. This skipping mechanism significantly improves responsiveness for clear detections without compromising precision for uncertain cases that require full processing.
Data Source
AI summary
Features are disclosed for detecting an event in input data using a cascade-based detection system. Detection of the event may be triggered at any stage of the cascade, and subsequent stages of the cascade are not reached in such cases. Individual stages of the cascade may be associated with detection thresholds for use in triggering detection of the event. The sequence of stages may be selected based on some observed or desired operational characteristic, such as latency or operational cost. In addition, the cascade may be modified or updated based on data received from client devices. The data may relate to measurements and determinations made during real-world use of the cascade to detect events in input data.