Hierarchical Appearance Model for Object Re-identification
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Solution Overview
Problem
Surveillance systems face challenges in accurately re-identifying objects across varying environmental conditions and object postures due to the complexity of comparing image details, with existing methods being inefficient or impractical as the number of images grows large.
Innovation Solution
A hierarchical appearance model is created with multiple layers, each containing nodes that provide information at different levels of detail, allowing for a coarse-to-fine approach to object re-identification, resistant to changes in environment and object posture, using statistical models to combine probabilities of match across layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed image comparison is performed to improve re-identification accuracy, then measurement precision improves, but device complexity and computational requirements increase prohibitively
Solution Approach 1:
The image is segmented into multiple histograms representing different visual features (color, texture, shape) at different levels of detail. This segmentation allows the system to compare specific features rather than entire images, reducing computational complexity while maintaining re-identification accuracy through multi-level histogram comparison.
Solution Approach 2:
The patent transforms the image comparison problem from spatial domain to statistical domain by creating histograms. This dimensional transformation converts pixel-level comparisons into distribution-based comparisons, significantly reducing computational requirements while preserving the ability to distinguish objects under varying conditions.
2Reliability
If multiple images are captured to improve re-identification accuracy under varying conditions, then reliability improves, but loss of time increases due to processing large numbers of images
Solution Approach 1:
Histograms are pre-computed from multiple images during a training phase, capturing the statistical distribution of visual features across different conditions. During re-identification, only the pre-computed histograms need to be compared, not the original images, significantly reducing processing time while maintaining reliability gained from multiple images.
Solution Approach 2:
Instead of storing and processing multiple original images, the system creates simplified histogram representations that copy the essential statistical information. These histogram copies can be quickly compared without requiring access to the full-resolution source images, reducing processing time while preserving re-identification reliability.
3Measurement precision
If comprehensive information is retained in appearance models to improve re-identification accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The appearance model is segmented into multiple histograms representing different visual features at different levels of detail. This segmentation allows comprehensive information to be organized in a structured manner that is more efficient to store and compare, reducing model complexity while maintaining the ability to capture comprehensive object characteristics for accurate re-identification.
Data Source
AI summary
A method for creating an appearance model of an object includes receiving an image of the object and creating a hierarchical appearance model of the object from the image of the object. The hierarchical appearance model has a plurality of layers, each layer including one or more nodes. Nodes in each layer contain information of the object with a corresponding level of detail. Nodes in different layers of the hierarchical appearance model correspond to different levels of detail.