Dictionary Learning for Person Re-Identification Across Domains
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Solution Overview
Problem
Existing person re-identification systems face challenges in robustly matching objects across cameras with different viewing angles, lighting conditions, and orientations due to the domain shift problem, which occurs when training and deployment environments differ, leading to poor performance without sufficient labeled training data in the target domain.
Innovation Solution
A method that learns a dictionary using both source and target domain data, where the dictionary is based on mean value differences, allowing for comparison code generation and object matching across domains without requiring extensive labeled data in the target domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dictionary is learned from labeled training samples in the target domain, then matching accuracy is improved, but the requirement for extensive labeled training data increases
Solution Approach 1:
The patent introduces source domain data as an intermediary to supplement the insufficient target domain labeled training data. By combining source domain data with target domain data in a unified dictionary learning framework, the system can learn effective appearance descriptors without requiring extensive labeled target domain data, thus resolving the contradiction between matching accuracy and data quantity requirement
Solution Approach 2:
The patent performs preliminary dictionary learning using source domain data before applying it to the target domain. This preliminary action allows the system to pre-learn general appearance features that can then be adapted to the target domain, reducing the need for extensive labeled target domain data while maintaining matching accuracy
2Quantity of substance
If a dictionary is learned from source domain data only, then the need for target domain training data is reduced, but domain shift problem causes poor performance
Solution Approach 1:
The patent merges source domain data and target domain data into a single unified dictionary learning process. By combining data from both domains while using domain-specific weighting and adaptation mechanisms, the system achieves both data efficiency and performance robustness, resolving the contradiction between reduced data requirement and maintained reliability
Solution Approach 2:
The patent employs parameter changes through domain-specific weighting factors and adaptation parameters that adjust the contribution of source and target domain data dynamically. This allows the system to leverage source domain data while adapting to target domain characteristics, maintaining performance robustness without requiring extensive target domain training data
Data Source
AI summary
A method of comparing objects in images. A dictionary determined from a plurality of feature vectors formed from a test image and codes formed by applying the dictionary to the feature vectors is received, the dictionary being based on a difference in mean values between the codes. Comparison codes are determined for the objects in the images by applying the dictionary to feature vectors of the objects in the images. The objects in the images are compared based on the comparison codes of the objects.