Re-identification System Direction Consistency Ranking
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Solution Overview
Problem
The accuracy of person re-identification across multiple videos taken from different viewpoints is reduced due to variations in the direction of the moving object, requiring significant labor to address this issue solely through re-identification model learning.
Innovation Solution
A re-identification system that calculates similarities and ranks features considering the direction of moving objects, using a processor to determine identity based on consistency with a ranking rule, improving the accuracy of the re-identification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If re-identification model learning is used to handle direction variations, then re-identification accuracy may improve, but a great deal of labor is required
Solution Approach 1:
The system performs preliminary actions by extracting direction information and pre-ranking similarities before final re-identification determination. The processor calculates direction of moving objects, ranks similarities based on direction consistency, and performs preliminary determination processes that reduce the need for extensive model learning and manual adjustment.
Solution Approach 2:
The system implements feedback mechanisms by calculating consistency between ranked similarities and direction information, then using this feedback to adjust the re-identification determination. The processor compares ranked similarities with direction data and uses this feedback loop to improve accuracy without requiring extensive retraining or manual intervention.
2Measurement precision
If direction information is considered in re-identification, then re-identification accuracy improves, but the complexity of the re-identification process increases
Solution Approach 1:
The system segments the re-identification process into distinct modules: direction extraction, similarity calculation, ranking based on direction consistency, and final determination. The processor divides the complex task into manageable segments where each component handles a specific aspect, making the overall process more manageable and maintainable while improving accuracy.
Solution Approach 2:
The system adds another dimension to the re-identification process by incorporating direction information as a separate feature space. Instead of only considering appearance features, the processor combines appearance similarity with directional orientation, creating a multi-dimensional assessment that improves accuracy without overwhelming complexity.
Data Source
AI summary
A re-identification system temporarily performs a re-identification process for determining whether two moving objects shown in a plurality of videos are identical or not. Similarities between the two moving objects in the re-identification process are ranked in consideration of a direction of each moving object. The rank is highest when the two moving objects are identical and the two moving objects are same in the direction. The rank is lowest when the two moving objects are not identical and the two moving objects are different in the direction. A ranking rule is that the rank is higher as the similarity is higher. The re-identification system calculates a degree of consistency between the ranking result and the ranking rule. Then, the re-identification system finally determines whether the two moving objects are identical or not based on the degree of consistency in addition to the similarities.