Human Component Detection via Segmented Feature Extraction
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Solution Overview
Problem
Current human detection methods face challenges in adapting to variations in human appearance due to different clothing styles and illumination conditions, and require robust features that capture characteristic patterns while maintaining real-time processing capabilities and accuracy, especially in complex backgrounds with occluding accessories.
Innovation Solution
A human component detection method and apparatus that utilize a training database to store positive and negative samples, calculate difference images, and extract feature populations using a sub-window processor, with a classifier learning a human component model for stable identification and real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional human detection methods use global models or local feature populations, then detection coverage is improved, but adaptability to variations in human appearance and illumination conditions deteriorates
Solution Approach 1:
The patent segments the human body into multiple components (head, torso, limbs) and detects each component separately using component-specific templates. This segmentation allows the system to handle variations in global appearance while maintaining accurate detection of individual body parts, resolving the contradiction between detection accuracy and adaptability to appearance variations.
Solution Approach 2:
The patent applies local quality by using different detection strategies for different body components. Each component (head, torso, limbs) has its own specialized template and detection parameters optimized for its specific characteristics. This local optimization enables the system to adapt to variations in each body part while maintaining overall detection accuracy.
2Reliability
If robust features are used to capture characteristic patterns, then detection reliability is improved, but processing complexity increases
Solution Approach 1:
The patent divides the complex human detection task into simpler sub-tasks by segmenting the body into components. Each component detection uses simplified templates and features, reducing overall processing complexity while maintaining reliability through the cumulative effect of detecting multiple components.
Solution Approach 2:
The patent focuses on detecting key body components (head, torso, limbs) rather than analyzing every detail of the entire human figure. This partial action approach captures the essential characteristic patterns needed for reliable detection while avoiding the computational burden of comprehensive analysis.
3Measurement precision
If component detection is used to segment human body, then detection accuracy is improved, but computational requirements increase
Solution Approach 1:
The patent segments the detection task into parallel component detections (head, torso, limbs) that can be processed simultaneously. This segmentation enables accurate component-level detection while maintaining real-time processing capability through parallel computation of multiple independent templates.
Data Source
AI summary
Disclosed are an apparatus and a method of detecting a human component from an input image. The apparatus includes a training database (DB) to store positive and negative samples of a human component, an image processor to calculate a difference image for the input image, a sub-window processor to extract a feature population from a difference image that is calculated by the image processor for the positive and negative samples of a predetermined human component stored in the training DB, and a human classifier to detect a human component corresponding to a human component model using the human component model that is learned from the feature population.