Dual-Camera Person Recognition with Infrared Cut-Off Filter
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Solution Overview
Problem
Current person re-identification algorithms face challenges in accurately identifying individuals in expanded near and far ranges due to small face images and obscuration by objects, and existing solutions that increase camera lens focal length reduce monitoring region coverage.
Innovation Solution
A person recognition device with a dual-camera setup, where one camera captures images in a near-infrared or UV range with a cut-off filter, generating a channel image through intelligent subtraction of images from different perspectives, enhancing image quality and texture recognition for effective re-identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera lenses have a larger focal length to improve face recognition accuracy, then measurement precision is improved, but the monitoring region coverage is reduced
Solution Approach 1:
The monitoring region is divided into multiple zones (near range, expanded near range, far range) that are captured by different camera units with different focal lengths. The first camera unit with larger focal length captures detailed images for near range, while the second camera unit with smaller focal length captures broader views for expanded near and far ranges. This segmentation allows each camera to operate at optimal focal length for its specific zone, resolving the contradiction between measurement precision and monitoring region coverage.
Solution Approach 2:
The system transitions from a single-camera approach to a multi-camera spatial arrangement, adding a dimensional aspect to the monitoring system. By positioning camera units at different locations and angles, the system achieves both high precision face recognition in near range and broad coverage in expanded near and far ranges simultaneously, effectively resolving the focal length coverage contradiction through spatial dimensionality.
2Adaptability or versatility
If face recognition algorithms are used in expanded near range or far range, then person re-identification capability is improved, but reliability deteriorates due to small face images and obscuration
Solution Approach 1:
The system segments the monitoring region into different ranges (near, expanded near, far) and assigns different camera units to capture each range optimally. The first camera unit captures high-detail images for near range identification, while the second camera unit captures broader views for expanded near and far ranges. This segmentation ensures that identification algorithms receive appropriately scaled and unobscured images for each range, maintaining reliability across all distances.
Solution Approach 2:
Different camera units are positioned and configured with specific optical properties tailored to their respective monitoring zones. The first camera unit is optimized for near range with higher resolution, while the second camera unit is optimized for expanded near and far ranges with broader field of view. This local quality optimization ensures that each zone receives the appropriate image quality needed for reliable identification, resolving the reliability issue in expanded ranges.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables robust person re-identification in expanded ranges by improving image quality and contrast, allowing for accurate identification of individuals based on texture and color features, even in challenging environments.
Implementation Method 1
The second camera has a cut-off filter. The cut-off filter is configured for cutting off wavelengths of incident light. The wavelength cutting takes place in a stop band.
Implementation Method 2
The evaluation module is configured to generate a channel image (difference image) of the portion based on the first monitoring image and the second monitoring image.
Data Source
AI summary
Person recognition device for person re-identification in a monitoring region, having a camera apparatus and an evaluation module, wherein the camera apparatus comprises a first camera unit and a second camera unit, wherein the first camera unit is configured to record a first monitoring image of a portion of the monitoring region, wherein the second camera unit is configured to record a second monitoring image of the portion of the monitoring region, wherein the camera apparatus is configured to feed the first monitoring image and the second monitoring image to the evaluation module, wherein the evaluation module is configured to re-identify a person in the monitoring region based on the first monitoring image and the second monitoring image, wherein the second camera unit has a cut-off filter for wavelength cutting of incident light in a stop band.