Cross-Modal Neural Image Analysis for Privacy-Safe Detection
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Solution Overview
Problem
Existing automated image analysis systems struggle to accurately identify elements of interest across different imaging modalities and maintain privacy by avoiding the use of certain types of data acquisition devices, such as cameras in sensitive areas.
Innovation Solution
A method and system utilizing two neural networks to process data from different imaging modalities (e.g., radar and camera) to extract features, find differences, and ascertain the presence of an element of interest, while iteratively optimizing network weights to enhance accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera data is used for image analysis, then identification accuracy is improved, but privacy concerns worsen in sensitive areas
Solution Approach 1:
The patent introduces radar data as an intermediary modality that can detect elements of interest without the privacy intrusion of camera data. The system processes radar data through neural networks to identify elements of interest, achieving the detection function while avoiding the harmful privacy effects of optical cameras in sensitive areas like bathrooms or changing rooms.
2Measurement precision
If multiple imaging modalities are processed, then identification accuracy across different scenes is improved, but system complexity worsens
Solution Approach 1:
The patent merges multiple imaging modalities (camera data and radar data) into a unified processing framework using neural networks. The system combines features extracted from different data sources to improve identification accuracy while managing complexity through integrated processing architecture that handles multiple modalities cohesively.
Solution Approach 2:
The patent segments the processing of different imaging modalities by using separate neural networks for camera data and radar data, then combines their outputs. This segmentation allows each network to specialize in its modality while the overall system achieves multi-modality accuracy, reducing the complexity burden on any single processing component.
3Measurement precision
If feature extraction is performed on multiple data types, then element identification accuracy is improved, but processing time worsens
Solution Approach 1:
The patent performs preliminary feature extraction on camera and radar data separately through dedicated neural networks before combining and comparing features. This preliminary processing organizes the data in advance, enabling more efficient final comparison and identification, thereby reducing the overall processing time despite handling multiple data types.
Data Source
AI summary
A method for automatically identifying elements in a scene, including obtaining first data relating to at least one first scene possibly including at least one element of interest, obtaining second data different from the first data and relating to a second scene including the at least one element of interest, processing, by a first neural network, at least some of the first data to automatically extract at least one first feature representing at least a part of the at least one first scene, processing, by a second neural network, at least some of the second data to automatically extract at least one second feature representing the element of interest, finding a difference between the at least one first feature and the at least one second feature, ascertaining whether or not the at least one element of interest is present in the at least one first scene, based on the difference and providing a human-sensible output indicative of whether or not the at least one element of interest is present in the at least one first scene.