Infrared Pedestrian Recognition in High-Temperature Backgrounds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image recognition technologies using infrared cameras struggle to accurately detect pedestrians when high temperature objects are present in the background, leading to improper detection of individuals.
Innovation Solution
A recognition processing apparatus and method that utilizes a first detection process for pedestrians outside high temperature regions and a second detection process for those inside, employing distinct detection models trained on images with and without high temperature objects in the background, respectively, to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single detection process is used for all regions in the video, then the device complexity is low, but the detection accuracy deteriorates when high temperature objects are present in the background
Solution Approach 1:
The video is divided into multiple regions based on temperature characteristics: high temperature regions and non-high temperature regions. Different detection processes are applied to each region type, with the first detection process used for non-high temperature regions and the second detection process used for high temperature regions. This segmentation resolves the contradiction by improving detection accuracy in high temperature regions without unnecessarily complicating the overall system.
Solution Approach 2:
Different detection processes with specialized characteristics are applied to different spatial regions of the video based on their thermal properties. The second detection process is specifically optimized for high temperature regions where it can distinguish pedestrians from hot backgrounds, while the first detection process handles normal regions. This local quality approach improves overall detection accuracy without requiring a completely complex system for all regions.
2Measurement precision
If different detection processes are used for high temperature and non-high temperature regions, then the detection accuracy improves, but the device complexity increases
Solution Approach 1:
The detection system segments the video into high temperature and non-high temperature regions, applying appropriate detection processes to each. This segmentation allows the system to use simpler detection in normal regions while reserving complex detection only where needed (high temperature regions), thus improving accuracy without proportionally increasing overall complexity.
Solution Approach 2:
The system changes detection parameters dynamically based on regional temperature characteristics. By adjusting which detection process is applied based on the thermal properties of each region, the system optimizes detection accuracy for different conditions without maintaining permanently complex operations across all regions, thereby managing system complexity effectively.
3Reliability
If pattern matching with a recognition dictionary is used, then the detection process is simple, but detection reliability deteriorates when high temperature objects are in the background
Solution Approach 1:
The system segments the video based on temperature regions and applies different detection approaches accordingly. In high temperature regions where simple pattern matching fails, the second detection process is used to reliably distinguish pedestrians from hot backgrounds. In non-high temperature regions, the simpler first detection process suffices, maintaining reliability without unnecessary complexity.
Solution Approach 2:
Different detection qualities are applied locally based on regional needs. The second detection process provides higher reliability specifically in high temperature regions where it is most needed, while the first detection process handles normal regions. This local quality approach improves overall reliability without requiring maximum complexity everywhere.
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
Improves the accuracy of pedestrian detection both inside and outside high temperature regions by leveraging specialized detection models tailored to each scenario, ensuring precise identification in challenging thermal imaging conditions.
Implementation Method 1
a video captured by an infrared camera
Data Source
AI summary
A recognition processing apparatus includes: a video acquisition unit that acquires a video captured by an infrared camera; a high temperature region detection unit that detects a high temperature region included in the video; a first detection unit that detects a person outside the high temperature region in the video by using a first detection process; and a second detection unit that detects a person inside the high temperature region in the video by using a second detection process different from the first detection process.