Cascade Test Ordering for Object Detection Runtime
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Solution Overview
Problem
Cascade architectures for object detection face inefficiencies in runtime optimization without compromising classification accuracy, particularly due to the placement of detectors with varying runtime and statistical dependencies between tests.
Innovation Solution
The method optimizes the ordering of tests in a cascade by considering both statistical dependencies and ordering constraints, using quality measures and series-parallel structures to minimize expected runtime, with algorithms for different dependency scenarios, including independent tests, series-parallel constraints, and general constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If detectors with high reject probability are placed first in the cascade, then classification accuracy is improved, but runtime increases due to placing computationally expensive detectors early
Solution Approach 1:
The patent changes the ordering parameter from reject probability alone to a composite metric that incorporates both reject probability and runtime characteristics. By redefining the sorting criterion to consider execution time, the system transforms the detector ordering problem into an optimization problem that balances accuracy and speed, resolving the contradiction between placing high-reject detectors first and minimizing runtime.
Solution Approach 2:
The patent introduces dynamic ordering where the cascade structure adapts to different input characteristics and operational requirements. The system can dynamically adjust the detector ordering based on runtime constraints, statistical dependencies among detectors, and performance requirements, making the cascade flexible rather than static in its detector arrangement.
2Measurement precision
If the cascade considers statistical dependencies between tests, then ordering accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex problem of optimizing detector ordering with statistical dependencies into manageable subproblems. It divides the detector set into groups based on their dependency relationships, allowing independent optimization within each group. This segmentation reduces the overall computational complexity while still capturing the essential statistical dependencies that improve ordering accuracy.
Solution Approach 2:
The patent introduces intermediary structures such as dependency graphs and conditional probability models that mediate between the raw statistical data and the final ordering decision. These intermediaries organize and pre-process the statistical dependency information, making it computationally tractable to incorporate into the ordering optimization without directly computing all possible permutations.
3Stability of the object's composition
If ordering constraints are enforced between detectors, then structural requirements are satisfied, but flexibility in optimization is reduced
Solution Approach 1:
The patent applies nested ordering where detectors are arranged in hierarchical levels with different constraint requirements. Inner levels enforce strict structural ordering constraints to satisfy compositional requirements, while outer levels provide flexibility for optimization. This nested structure allows simultaneous satisfaction of structural requirements and optimization goals by operating at different hierarchical levels.
Solution Approach 2:
The patent applies different ordering strategies to different subsets of detectors based on their local characteristics and constraints. Rather than enforcing a single global ordering rule, the system allows local variations in ordering criteria for different detector groups, enabling structural requirements to be met in critical regions while maintaining optimization flexibility in other regions.
Data Source
AI summary
Methods for optimizing the cost of executing a set of tests including finding the optimal ordering of the tests for some important cases such as set of tests having series-parallel structure with no statistical dependencies, and near-optimal orderings for the rest of the cases, such that the resources required for executing the tests are minimized.