Weak Classifier Pipeline for Embedded Object Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing object classification methods for embedded systems face inefficiencies in processing time due to varying completion times of weak classifiers across categories, leading to idle processing devices and challenges in real-time classification for multiple categories.
Innovation Solution
The technique involves grouping weak classifiers into two logical groups and performing pipeline processing, where the first group's classification results determine which categories require processing by the second group, allowing for efficient decision-making and reduced processing time by optimizing the sequence of classifier operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weak classifiers for multiple categories are processed simultaneously by separate processing devices, then classification accuracy for all orientation variations is improved, but processing devices remain idle for long periods due to varying completion times
Solution Approach 1:
The patent segments the classification process into multiple stages, with each stage containing a subset of weak classifiers. Processing devices handle one stage at a time and pass results to the next stage, allowing continuous utilization without idle time while maintaining comprehensive classification coverage across all categories.
Solution Approach 2:
The patent implements continuous processing where processing devices continuously perform classification tasks across multiple stages. As soon as one stage completes, the device immediately transitions to the next stage, eliminating idle time and ensuring continuous useful action throughout the classification pipeline.
2Adaptability or versatility
If the number of weak classifiers is increased to cover more categories, then classification coverage is improved, but processing time increases
Solution Approach 1:
The patent divides the large set of weak classifiers into multiple smaller stages. Each stage processes a subset of classifiers, allowing the system to maintain comprehensive category coverage while reducing the processing time required for each individual stage, thereby enabling parallel and efficient processing.
Solution Approach 2:
The patent performs preliminary classification in early stages using a subset of weak classifiers to filter and identify potential matches. This preliminary action reduces the burden on subsequent stages, allowing the system to handle comprehensive category coverage without proportionally increasing total processing time.
Data Source
AI summary
In an information processing apparatus that includes sequences of weak classifiers which are logically cascade-connected in each sequence and the sequences respectively correspond to categories of an object and in which the weak classifiers are grouped into at least a first group and a second group in the order of connection, classification processing by weak classifiers belonging to the first group of respective categories is performed by pipeline processing. Based on the processing results of the weak classifiers belonging to the first group of the respective categories, categories in which classification processing by weak classifiers belonging to the second group is to be performed are decided out of the categories. The classification processing by the weak classifiers respectively corresponding to the decided categories and belonging to the second group is performed by pipeline processing.