Low Variance Region Detection for High Variance Object Identification
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Solution Overview
Problem
Current computer vision techniques for autonomous vehicles face challenges in accurately detecting high variance regions due to significant variations in objects, such as pedestrians, which leads to reduced safety and increased computational resources when trying to model these regions directly.
Innovation Solution
The approach involves detecting low variance regions first, using machine learned algorithms to identify sub-regions with low variance, which are then used to determine the presence of high variance regions, thereby improving detection accuracy without the need for extensive training data and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computer vision techniques directly model high variance regions, then detection coverage is improved, but measurement precision deteriorates due to significant variations in objects
Solution Approach 1:
The patent segments high variance regions into multiple low variance sub-regions. Instead of attempting to detect entire pedestrians (high variance) directly, the system divides them into body parts such as heads, torsos, and limbs (low variance). Each sub-region is detected separately with higher precision, and then integrated to form the complete object detection. This segmentation approach resolves the contradiction by maintaining detection coverage while improving measurement precision through localized detection.
Solution Approach 2:
The patent applies local quality by training detection models specifically for low variance sub-regions rather than using a single general model for high variance regions. Each sub-region detector is optimized for its specific local characteristics, allowing the system to achieve high detection accuracy for individual body parts while maintaining comprehensive coverage when all sub-regions are combined.
2Adaptability or versatility
If computer vision techniques directly model high variance regions, then detection coverage is improved, but computational resources increase
Solution Approach 1:
The patent segments the complex task of detecting high variance regions into multiple simpler tasks of detecting low variance sub-regions. This segmentation reduces the computational burden by allowing each sub-region detector to be smaller and more efficient, while the overall system maintains comprehensive detection coverage through the combination of multiple specialized detectors.
Solution Approach 2:
The patent uses multiple copies of simplified detection models for low variance sub-regions rather than a single complex model for high variance regions. Each sub-region detector is a simplified copy optimized for specific local features, and multiple copies work together to achieve the functionality of a comprehensive high-variance detector with reduced computational requirements for each individual model.
3Adaptability or versatility
If computer vision techniques directly model high variance regions, then detection coverage is improved, but training data requirements increase
Solution Approach 1:
The patent segments the training problem into smaller sub-problems by training separate detectors for low variance sub-regions. This segmentation reduces the quantity of training data needed because each sub-region detector learns from a more constrained and consistent data distribution, making the learning task easier and requiring fewer examples to achieve high performance.
Solution Approach 2:
The patent changes the parameters of the detection problem by transforming high variance region detection into low variance sub-region detection. This parameter transformation reduces the variance in training data for each sub-region, allowing models to learn more effectively from smaller datasets and reducing the overall training data requirements while maintaining comprehensive detection coverage.
Data Source
AI summary
Using detection of low variance regions for improving detection is described. In an example, sensor data can be received from a sensor associated with a vehicle. The sensor data can represent an environment. An indication of a low variance region associated with the sensor data can be determined and an indication of a high variance region associated with the sensor data can be determined based at least in part on the indication of the low variance region. The vehicle can be controlled based on at least one of the sensor data or the indication of the high variance region.