Cascade Classifier Score Accumulation for Efficient Object Detection
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Solution Overview
Problem
Existing machine learning-based object detection techniques are computationally expensive, particularly in resource-limited environments, and lack efficient conditional branching and accumulation of scores across stages, leading to suboptimal performance in mobile and wearable devices.
Innovation Solution
A hardware engine is configured to implement multiple cascade classifiers with conditional branching and accumulation capabilities, allowing for dynamic evaluation path selection and score accumulation across stages based on outcome conditions, thereby optimizing resource usage and improving detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple cascade classifiers are executed independently to improve detection accuracy, then detection reliability is improved, but computational cost and resource consumption increase
Solution Approach 1:
The patent implements dynamic conditional branching between cascade classifiers based on detection outcomes. The system transitions from static independent execution to dynamic selective execution, where the path taken through the classifier cascade is determined by previous stage results. This allows the system to adaptively allocate computational resources based on actual detection needs, improving reliability while reducing unnecessary computations.
Solution Approach 2:
The patent changes the execution parameter from fixed independent execution to conditional dependent execution. By introducing outcome conditions as control parameters, the system can modify its execution path based on detected object characteristics, enabling more efficient resource utilization while maintaining high detection accuracy through selective classifier application.
2Device complexity
If cascade classifiers are executed without conditional branching to simplify control flow, then device complexity is reduced, but detection efficiency and resource management deteriorate
Solution Approach 1:
The patent introduces dynamic control flow through conditional branching, allowing the system to adapt its execution path based on detection outcomes. This dynamic approach improves detection efficiency by selecting appropriate classifiers based on actual needs, while the modular cascade structure keeps the overall system complexity manageable through organized hierarchical design.
Solution Approach 2:
The patent segments the detection system into multiple cascade classifiers organized in a hierarchical structure. This segmentation allows complex detection tasks to be divided into manageable stages, where each stage handles specific detection criteria. The modular architecture manages control flow complexity while enabling efficient resource allocation through conditional progression between segments.
3Ease of operation
If scores from evaluation stages are not accumulated to maintain simple processing, then processing simplicity is preserved, but detection accuracy and reliability deteriorate
Solution Approach 1:
The patent implements continuous accumulation of scores across evaluation stages, where detection confidence is aggregated throughout the cascade process. This continuous accumulation ensures that detection accuracy is maintained by considering evidence from all stages, while the systematic approach to score aggregation keeps the processing logic clear and manageable through structured accumulation operations.
Data Source
AI summary
Apparatus, methods, systems, and instructions stored on computer-readable medium are presented for performing classification. A hardware engine may be configurable to implement multiple stages of a cascade classifier including a first stage and a second stage. The hardware engine may be configurable to (a) access a value indicative of whether to accumulate, and (b) responsive to the value indicative of whether to accumulate meeting a continue evaluation condition, (i) access a first numeric value obtained from evaluation of the first stage of the cascade classifier, (ii) accumulate the first numeric value with a second numeric value obtained from evaluation of the second stage of the cascade classifier to generate an accumulated value, and (iii) utilize the accumulated value to determine an outcome for the second stage of the cascade classifier.