Robotic Feature Identification With Human-Aware Image Censoring
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Solution Overview
Problem
Existing robotic systems face challenges in accurately identifying features while preserving human confidentiality, as facial censoring methods often obscure or remove important features and hinder training of neural networks.
Innovation Solution
A method for robotic devices that uses multiple sensors to detect dynamic objects, determines if they are human or inanimate, and adjusts image processing to preserve confidentiality by selectively disabling lights or censoring images, ensuring accurate feature identification without compromising human anonymity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If facial censoring methods are applied to preserve human confidentiality, then human anonymity is maintained, but feature identification accuracy deteriorates
Solution Approach 1:
The system segments the image processing task by using a second sensor (e.g., LiDAR, depth camera) to detect dynamic objects and determine if they are human, separating the confidentiality protection function from the feature identification function. This allows selective application of censoring only when humans are detected, preserving features in non-human images.
Solution Approach 2:
The system dynamically adjusts image processing based on real-time detection results. The processor conditionally applies facial censoring only when a human is detected in the field of view, rather than uniformly censoring all images. This dynamic approach maintains feature identification accuracy for non-human objects while protecting human confidentiality.
2Productivity
If images are captured and processed for feature identification, then robot task performance is improved, but human privacy is compromised
Solution Approach 1:
The system performs preliminary detection using a second sensor to identify dynamic objects and determine if they are human before proceeding with feature identification processing. This preliminary action allows the system to prepare appropriate processing methods in advance, ensuring both productivity and privacy protection.
Solution Approach 2:
The second sensor acts as an intermediary between the first sensor (image capture) and the feature identification process. It provides additional information about the presence of humans, enabling the processor to make informed decisions about whether to apply confidentiality measures, thus mediating between productivity and privacy concerns.
3Measurement precision
If multiple sensors are used to detect dynamic objects, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The second sensor serves multiple functions: detecting dynamic objects, determining if they are human, and providing spatial information for field of view analysis. This multi-functionality reduces the need for additional specialized sensors, managing complexity while maintaining detection accuracy.
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
Preserves useful imagery for feature identification, maintains human confidentiality, and enhances robot task performance by providing secure methods for identifying features within environments, while improving human-robot interaction.
Implementation Method 1
The second sensor includes a LiDAR sensor
Implementation Method 2
the second sensor includes a thermal imaging camera
Implementation Method 3
the second sensor includes an ultrasonic sensor
Data Source
AI summary
Systems and methods for data and confidentiality preservation for feature identification by robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a method for determining if an image depicts a human is disclosed. The determination may be utilized to either (i) censor the face of the human, or (ii) identify features within the image. The determination enhances feature identification by ensuring only uncensored and unobscured images are provided to one or more models for the identification to preserve human confidentiality.