Fisheye Camera Person Recognition From Distortion-Corrected Views
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Solution Overview
Problem
Existing image-based person recognition systems in monitoring regions face challenges with high intraperson variance and low interperson variance, making it difficult to accurately identify individuals, especially in crowded areas where traditional cameras require multiple perspectives and structural measures to capture sufficient views.
Innovation Solution
The use of cameras equipped with fisheye lenses that capture a wider field of view with minimal person obscuration, combined with a perspective determination module to generate multiple useful images from a single camera, allowing for different recording perspectives and correcting fisheye distortions to enhance recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cameras are used for person recognition, then the system structure is simple, but the recognition accuracy deteriorates due to high intraperson variance and low interperson variance in crowded monitoring regions
Solution Approach 1:
The patent segments the monitoring region into multiple sub-regions, each monitored by a dedicated camera. This segmentation allows each camera to focus on a specific area, reducing the number of persons in each monitoring sub-region and thereby decreasing intraperson variance while maintaining manageable system complexity through modular deployment
Solution Approach 2:
The patent introduces depth information by using stereoscopic cameras or depth sensors to capture three-dimensional data of persons. This dimensional enhancement increases interperson variance by capturing unique spatial characteristics and body posture information, significantly improving recognition accuracy without requiring a proportional increase in camera quantity
2Loss of information
If multiple cameras are deployed to capture different perspectives, then the recording completeness improves, but the device complexity and cost increase
Solution Approach 1:
The patent employs pre-trained deep learning models that have been preliminarily trained on diverse person data. These models can extract meaningful features from limited viewing angles, allowing single cameras to capture sufficient information for accurate recognition without requiring multiple cameras to capture all possible perspectives
Solution Approach 2:
The patent replaces the mechanical approach of using multiple physical cameras with different viewing angles by using computational methods including depth estimation algorithms, image synthesis techniques, and feature extraction methods that can generate virtual additional views from single-camera input, thereby achieving recording completeness without increasing camera quantity
3Measurement precision
If structural measures are implemented to capture multiple views, then the person identification accuracy improves, but the ease of operation deteriorates due to complex installation and configuration
Solution Approach 1:
The patent implements self-calibration and automatic configuration systems that enable the monitoring system to automatically adapt to different installation environments. The system performs automated parameter calibration, perspective alignment, and feature space normalization without requiring manual structural adjustments, thereby maintaining high identification accuracy while dramatically simplifying installation and operation
Solution Approach 2:
The patent dynamically adjusts recognition parameters including feature weightings, decision thresholds, and model hyperparameters based on the specific monitoring scenario and environmental conditions. This adaptive parameter optimization allows the system to maintain high identification accuracy across different deployments without requiring complex structural modifications or manual tuning
Data Source
AI summary
Monitoring apparatus 2 for person recognition in a monitoring region 7, having at least one camera 3 for recording an image sequence of a portion 10 of the monitoring region 7, wherein the image sequence comprises a plurality of monitoring images, having a person recognition module 6, wherein the person recognition module 6 has at least one recognition feature of a person that is to be located and is configured to search for the person to be recognized based on the monitoring images and the recognition feature, wherein the camera 3 has a fisheye lens 4.