Person Recognition Model Training with Adaptive Camera Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing monitoring systems face challenges in seamlessly tracking and identifying individuals across multiple cameras due to varying lighting conditions and limited data variability, leading to potential misidentification and loss of tracking accuracy in rapidly changing scenes.
Innovation Solution
A method for training a person recognition model using images from cameras, which involves reading detection signals, collecting image signals, adapting the model with control parameters for active camera control, and extracting specific personal features to enhance data variability and robustness, allowing seamless tracking across a camera network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a person recognition model is trained using standard image data from monitoring cameras, then the model can identify persons in normal conditions, but the model fails to maintain recognition accuracy under varying lighting conditions and across different cameras in a network
Solution Approach 1:
The system performs preliminary action by actively controlling the camera to capture additional training images under diverse lighting conditions before the recognition task. The camera automatically adjusts exposure, gain, and other parameters to pre-capture a comprehensive dataset that includes various illumination scenarios, ensuring the model is prepared for future recognition tasks under varying light conditions.
Solution Approach 2:
The system applies dynamics by making the camera control parameters adaptive and adjustable in real-time. The camera automatically adjusts exposure time, gain, and other recording parameters based on current lighting conditions to capture optimal training images. This dynamic adjustment ensures the collected data reflects the actual varying conditions the model will encounter during deployment.
2Ease of operation
If monitoring systems use fixed camera settings to capture images for person recognition, then the system is simple to operate, but the data variability is insufficient for training robust recognition models
Solution Approach 1:
The system implements self-service by enabling the camera to automatically control its own recording parameters without manual intervention. The camera autonomously adjusts exposure, gain, and other settings based on real-time lighting conditions to capture diverse training images. This self-service mechanism maintains operational simplicity while significantly increasing data variability for model training.
Solution Approach 2:
The system applies parameter changes by dynamically modifying camera recording parameters such as exposure time, gain, and aperture during image capture. These parameter variations are automatically adjusted based on lighting conditions, enabling the collection of diverse training data with sufficient variability while maintaining ease of operation through automated control.
3Reliability
If the camera actively adjusts recording parameters to capture diverse training images, then data variability and model robustness improve, but the device complexity increases
Solution Approach 1:
The system applies universality by integrating multiple functions into the camera unit itself. The camera simultaneously performs standard monitoring, automatic exposure control, gain adjustment, and training image capture with diverse parameter variations. This multi-functionality consolidates what would otherwise require separate systems into a single device, reducing overall system complexity while maintaining recognition robustness.
Solution Approach 2:
The system implements feedback by using the detected lighting conditions and image quality metrics to automatically adjust camera recording parameters. The camera continuously monitors the scene and adjusts exposure, gain, and other parameters in real-time to capture optimal training images. This closed-loop feedback mechanism automates the complex parameter adjustments, reducing the need for manual intervention while improving recognition robustness.
4Adaptability or versatility
If multiple cameras in a network use different recording properties, then each camera can optimize for its specific viewing conditions, but person tracking becomes inconsistent across the network
Solution Approach 1:
The system applies parameter changes by standardizing the recording parameters across all cameras in the network. Each camera adjusts its exposure, gain, and other parameters to match a common reference configuration, ensuring consistent image characteristics across different locations. This standardization maintains tracking consistency while allowing each camera to optimize for its local lighting conditions through controlled parameter adjustments.
Data Source
AI summary
A method for training a person recognition model using images from a camera 100, wherein the method has at least a reading-in step in which a detection signal 135 representing a detected person within a monitoring area of at least the camera 100 in a camera network is read in. The method also has at least a collecting step in which a plurality of image signals 140 from the camera 100 are collected using the detection signal 135 which has been read in, wherein the collected image signals 140 represent a recorded image section from each image from the camera 100. Finally, the method has at least an adapting step in which the person recognition model is adapted using the collected image signals 140 in order to recognize the detected person in an image from the camera 100 or from an at least second camera in the camera network.