Adaptive Channel Features for Real-Time Object Detection
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Solution Overview
Problem
Existing object detection systems for advanced driver assistance systems and computer vision applications face challenges in achieving real-time performance on low-power mobile or embedded devices due to high computational requirements, especially when detecting objects like vehicles and pedestrians on roads.
Innovation Solution
The system employs a boosting classifier with adaptive channel features, using Aggregate Channel Features, Filtered Channel Features, and Adaptive Channel Features in a sliding window approach, optimized by camera calibration parameters to reduce computational load and leverage perspective effects, allowing for efficient object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional object detection methods are used, then detection accuracy can be maintained, but computational requirements become too high for real-time performance on low-power devices
Solution Approach 1:
The patent segments the feature extraction process into three distinct stages: Aggregate Channel Features (ACF) for initial fast filtering, Filtered Channel Features (FCF) for intermediate processing, and Adaptive Channel Features (AdCF) for final refinement. This segmentation allows the system to process only promising regions through computationally expensive operations, reducing overall energy consumption while maintaining real-time performance on low-power devices.
Solution Approach 2:
The patent applies partial action by selectively applying adaptive channel features only to candidate regions that pass through the ACF and FCF filtering stages. Instead of computing all possible features for all image regions, the system performs feature adaptation only where needed, significantly reducing computational load and energy consumption while maintaining detection accuracy.
2Measurement precision
If computationally expensive features are used for all sliding windows, then detection accuracy improves, but the number of operations increases making real-time processing impossible
Solution Approach 1:
The patent implements local quality by applying different feature extraction strategies to different regions of the image based on their likelihood of containing objects. High-quality adaptive features are applied only to candidate regions identified by faster ACF and FCF filters, while non-candidate regions are quickly rejected with simpler features. This local differentiation maintains detection accuracy for potential objects while maximizing processing throughput.
Solution Approach 2:
The patent performs preliminary action through the ACF and FCF filtering stages that precede the computationally expensive AdCF computation. These preliminary filters quickly identify and reject non-candidate regions, ensuring that adaptive features are only computed for regions with high object probability. This preliminary sorting dramatically reduces the number of operations required while maintaining detection accuracy.
3Measurement precision
If adaptive features are computed for every region, then detection precision improves, but computational complexity becomes unmanageable
Solution Approach 1:
The patent segments the computational complexity by dividing feature extraction into three hierarchical levels. The computationally intensive adaptive feature computation is segmented to apply only to candidate regions, while the majority of image regions are processed with simpler ACF and FCF methods. This segmentation reduces overall computational complexity while preserving feature discrimination precision where it matters most.
Solution Approach 2:
The patent introduces ACF and FCF as intermediary feature sets that bridge the gap between simple and complex feature extraction. These intermediary features serve as a filtering mechanism, identifying candidate regions that warrant further analysis with adaptive features. This intermediary approach reduces computational complexity by eliminating the need to compute adaptive features for all regions while maintaining precision for potential objects.
Data Source
AI summary
In accordance with some embodiments Adaptive Channel Features may be implemented by determining random features. The random features may be determined by defining a maximum allowed feature size of training samples. Then random filter positions of a training sample are sampled. Thereafter, pixel weights in a patch of the maximum allowed feature size is calculated. A feature is selected for applying a boosted classifier.