Boosted Chain Cascade Hybrid Feature Object Detection
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Solution Overview
Problem
Existing object detection algorithms in computer vision struggle to efficiently detect objects in real-time on low-power mobile or embedded devices, particularly for tasks like traffic sign recognition, due to high computational requirements and power consumption.
Innovation Solution
The use of a boosted chain cascade with hybrid features, where faster features are applied in initial boosted chains and more computationally expensive features are reserved for later stages, along with a sliding window technique that scales images and employs a boosted tree classifier to determine object presence, optimizing detection speed and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more computationally expensive features are used in all boosted chains, then detection accuracy is improved, but detection speed decreases and power consumption increases
Solution Approach 1:
The patent divides the feature extraction process into multiple boosted chains, where each chain uses a different level of computational complexity. The first few chains use fast, simple features for initial filtering, while later chains use more computationally expensive features for refined detection. This segmentation allows the system to achieve high accuracy without requiring all chains to use the most expensive features, thus maintaining detection speed.
Solution Approach 2:
Different regions of the feature space are treated with different levels of computational effort. The patent applies simple features in early stages when processing most image patches, and reserves complex features for later stages when only promising candidates remain. This local quality approach ensures computational resources are concentrated where they provide the most value for accuracy while minimizing overall computational cost.
2Measurement precision
If more computationally expensive features are used in all boosted chains, then detection accuracy is improved, but power consumption increases
Solution Approach 1:
The patent segments the detection pipeline into multiple boosted chains with varying computational requirements. By distributing features across chains rather than applying expensive features uniformly, the system reduces peak power consumption and average energy usage while maintaining the accuracy benefits of complex features in critical detection stages.
Solution Approach 2:
The patent applies computationally expensive features partially - only in later boosted chains when necessary for accurate detection, rather than applying them excessively across all chains. This partial application strategy achieves the required detection accuracy while significantly reducing power consumption compared to universal application of complex features.
3Measurement precision
If all boosted chains are evaluated for every image patch, then detection accuracy is improved, but detection time increases
Solution Approach 1:
The patent segments the evaluation process into a hierarchical structure where early boosted chains with simple features act as filters. Most image patches are rejected by these initial chains, preventing them from reaching later, more time-consuming chains. This segmentation dramatically reduces the average number of chains evaluated per patch while preserving detection accuracy for true positive cases.
Solution Approach 2:
The patent performs preliminary filtering using fast, simple features in early boosted chains before applying more complex features in later chains. This preliminary action eliminates the majority of negative patches early in the process, so that time-consuming complex feature evaluation is performed only on a small subset of promising candidates, reducing overall detection time.
Data Source
AI summary
A specified object in still images or video may be detected using a sliding search window technique, applied to the original image and its downscaled versions, in order to detect objects of different sizes. At each scale and each position of the sliding window, the technique may use a boosted tree classifier to determine whether the window contains the object. It may exit earlier if some intermediate sum falls below the certain threshold. To accelerate object detection, hybrid features are used in different boosted chains. For first boosted chains, the fastest features may be applied and then, after more complex (but slower) features and for the last few chains, the most powerful feature (but most computationally expensive) is used. This strategy may improve the speed of detection because for a majority of checking windows, only first boosted chains are used and so only the fastest features are calculated in some embodiments.