Cross-Modality Object Re-Identification via Discriminative Feature Vectors
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Solution Overview
Problem
Current monitoring systems face challenges in accurately and efficiently comparing and identifying objects across different sensor modalities, leading to inefficient and inaccurate object identification and re-identification processes.
Innovation Solution
A method involving the extraction of discriminative features from images using neural networks, which generates feature vectors for comparison, allowing for the identification of objects across various sensor types and modalities, and enabling efficient and accurate object re-identification by storing these vectors in a database for later matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If objects are tracked across different sensor modalities using traditional methods, then object identification can be performed, but accuracy deteriorates due to modality differences and computational demands increase
Solution Approach 1:
The patent extracts discriminative features from sensor data to create feature vectors that represent objects in a modality-agnostic space. By extracting only the essential discriminative features rather than processing entire sensor datasets, the system achieves accurate cross-modality object identification while significantly reducing computational demands.
Solution Approach 2:
The patent transforms sensor data from different modalities into a unified feature vector representation space. By changing the parameter representation from raw sensor data to standardized feature vectors, the system enables accurate comparison and matching of objects across different sensor types without being constrained by modality-specific characteristics.
2Measurement precision
If traditional object comparison methods are used across sensor modalities, then identification can be performed, but accuracy deteriorates due to modality differences
Solution Approach 1:
The patent creates a universal feature vector representation that can represent objects across multiple sensor modalities (e.g., camera, LIDAR, radar). This universal representation enables the same object identification algorithm to work effectively across different sensor types, improving both accuracy and cross-modality compatibility simultaneously.
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation between different sensor modalities. Instead of directly comparing raw data from different sensors, the system translates all sensor data into a common feature vector space, enabling accurate cross-modality object matching while accommodating differences in sensor characteristics.
3Measurement precision
If detailed feature extraction is performed for accurate object identification, then identification accuracy improves, but processing time increases
Solution Approach 1:
The patent extracts only the most discriminative features from sensor data that are essential for object identification, rather than processing all available data in detail. This selective feature extraction maintains high identification accuracy while significantly reducing the computational burden and processing time required.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-readable media, for obtaining a first image from a first sensor; detecting a first object within the first image; extracting discriminative features of the first object into a first feature vector; obtaining a second image from a second sensor; detecting a second object within the second image; extracting discriminative features of the second object into a second feature vector; and determining, based on a comparison between the first feature vector and the second feature vector, that the second object is same as the first object.