Shared Weak Classifier Ensemble for Multi-Class Object Detection
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Solution Overview
Problem
Existing multi-class object detection methods using boosted classifiers face significant memory and computational complexity issues, making real-time detection infeasible when dealing with multiple object classes, such as cars, trucks, pedestrians, and traffic signs, due to the need for separate ensembles of weak classifiers and weights for each class.
Innovation Solution
A method that employs a common ensemble of weak classifiers shared across all object classes, with individual weighting schemes for each class, reducing computational complexity by re-adapting weights using AdaBoost and selecting weak classifiers through techniques such as distinguished class selection, symmetric training, or union-based training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate ensembles of weak classifiers and weights are trained for each object class, then detection accuracy for each class is improved, but memory requirements and computational complexity scale linearly with the number of classes, making real-time detection infeasible
Solution Approach 1:
The patent merges the weak classifier ensembles across multiple object classes by training a single common ensemble of weak classifiers that can be reused for detecting different classes. Instead of maintaining separate ensembles for each class, the system shares the same set of weak classifiers while using class-specific weighting factors, thereby reducing computational complexity while maintaining detection accuracy.
Solution Approach 2:
The common ensemble of weak classifiers serves multiple functions by being applied to detect different object classes through different weighting schemes. The same weak classifiers trained on combined data from multiple classes can universally detect various object types when appropriate class weights are applied, eliminating the need for class-specific classifier ensembles.
2Reliability
If separate ensembles of weak classifiers are trained for each object class, then detection performance for each class is optimized, but memory requirements increase significantly, preventing real-time processing
Solution Approach 1:
The patent combines multiple class-specific weak classifier ensembles into a single shared ensemble. By training one common ensemble that incorporates weak classifiers from all object classes and reusing it across classes with appropriate weighting, the system dramatically reduces the total number of classifiers stored in memory while maintaining high detection performance for each class.
3Measurement precision
If individual weighting schemes are used for each object class with separate ensembles, then detection accuracy is maintained, but computational resources required for real-time detection become excessive
Solution Approach 1:
The patent merges the computational burden by using a single common weak classifier ensemble that is evaluated once per image patch, with class-specific weights applied during the evaluation stage. This approach maintains detection accuracy through individual weighting schemes while reducing the number of classifier evaluations needed, thereby enabling real-time processing.
Data Source
AI summary
A method for training a system for detecting multi-class objects in an image or a video sequence is described. A common ensemble of weak classifiers for a set of object classes is identified. For each object class, a separate weighting scheme is adapted for the ensemble of weak classifiers. A method for detecting objects of multiple classes in an image or a video sequence is also disclosed. Each class is assigned a detector that is implemented by a weighted combination of weak classifiers such that all of the detectors are based on a common ensemble of weak classifiers. Then weights are individually set for each class.