Adaptive Cross Partition for Object Detection Weak Classifiers
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Solution Overview
Problem
Conventional object detection systems face challenges in adapting to the varying distribution of training samples during the boosting procedure, as they rely on predefined partitions of equal width for 1-D Haar-like features, which limits their ability to distinguish object patterns effectively and leads to inefficiencies in computational time and accuracy.
Innovation Solution
The system reuses a 1-dimensional feature from a previously learned weak classifier to construct a 2-dimensional feature space, cross-partitions this space to create uneven sub-regions with uniform sample distribution, and learns optimal outputs for each block independently within a boosting framework, enabling feature sharing and adaptation to distribution changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a predefined partition of equal width is used for 1-D Haar-like features, then the system structure is simple and easy to implement, but the system fails to adapt to the varying distribution of training samples during boosting procedure, leading to reduced detection accuracy
Solution Approach 1:
The partition structure is transformed from static (predefined equal width) to dynamic (adaptive widths learned during boosting). The weak classifier learns optimal partition points that adapt to the current sample distribution, allowing the system to dynamically adjust to varying distributions throughout the boosting procedure while maintaining reasonable computational complexity
Solution Approach 2:
The partition parameters (widths and boundaries) are changed from fixed predefined values to learned adaptive values. The system learns optimal partition parameters during the boosting process that reflect the actual distribution of training samples, thereby improving detection accuracy without requiring complex manual tuning
2Measurement precision
If a much finer partition is used for each 1-D feature to mitigate the rough distinction problem, then the partition detail increases, but the partition cannot adapt to the distribution of training samples and its variation during boosting procedure
Solution Approach 1:
The partition structure is transformed from static (predefined equal width) to dynamic (adaptive widths learned during boosting). The weak classifier learns optimal partition points that adapt to the current sample distribution, allowing the system to dynamically adjust to varying distributions throughout the boosting procedure while maintaining reasonable computational complexity
Solution Approach 2:
The system performs self-adjustment by automatically learning optimal partition parameters from the training data during the boosting process. The weak classifier adapts to the sample distribution on its own without requiring external intervention or manual tuning, achieving both fine partition detail and adaptability
3Ease of operation
If binary partition of each feature space is learned sequentially with pure supervision, then the learning process is straightforward, but no features are shared between weak classifiers, leading to increased computational time
Solution Approach 1:
The system merges the learning processes of consecutive weak classifiers by enabling feature sharing. The 2-D feature space construction allows a feature learned in one weak classifier to be reused in subsequent classifiers, combining their learning processes and reducing redundant computation while maintaining the sequential learning framework
Solution Approach 2:
Features are given universal applicability by allowing them to serve multiple weak classifiers. A feature learned in one context can be reused across different weak classifiers, making the feature extraction process multi-functional and reducing the overall computational burden of the detection system
4Productivity
If conventional weak classifiers are used with 1-D Haar-like features, then the system is computationally efficient, but it cannot achieve effective adaptation to varying sample distributions, resulting in lower detection accuracy
Solution Approach 1:
The system transitions from 1-D feature partitions to 2-D feature space partitions. By constructing a 2-D feature space from consecutive weak classifiers and learning joint partitions, the system gains an additional dimension for adaptation, enabling it to capture complex sample distributions more effectively while maintaining computational efficiency through the boosting framework
Data Source
AI summary
Systems and methods are disclosed to perform object detection for images from an image sensor by reusing a 1-dimensional feature from a previously learned weak classifier and selecting a new feature to construct a 2-dimensional feature space; and cross partitioning the 2-dimensional space to learn optimal outputs for instances in each domain within a boosting framework.