Multi-Scale Pedestrian Re-Identification Using Transformer Feature Cascading
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Solution Overview
Problem
Existing pedestrian re-identification methods based on deep convolutional networks lose fine-grained features during downsampling, leading to reduced recognition rates, while Transformer-based methods fail to fully utilize multi-scale features and structural information.
Innovation Solution
A Transformer-based multi-scale pedestrian re-identification method is proposed, utilizing a feature cascading module and local feature multi-scale fusion module to preserve low-dimensional details, incorporate high-dimensional features, and guide the model to focus on different scales, incorporating learnable tokens and multi-layer Transformer blocks for robust feature representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep convolutional networks are used for pedestrian re-identification, then the model can process images efficiently, but fine-grained features are lost during downsampling operations
Solution Approach 1:
The patent segments the image processing into multiple scale levels, extracting features at different downsampling stages (1/4, 1/8, 1/16 scale) rather than relying on a single downsampling operation. This segmentation allows preservation of fine-grained features at higher scales while maintaining processing efficiency at lower scales.
Solution Approach 2:
The patent introduces a multi-scale dimension to the feature extraction process, transforming the single-scale feature representation into a multi-scale feature pyramid. This adds a scale dimension that allows the model to access both fine-grained and coarse-grained features simultaneously.
2Reliability
If Transformer-based methods segment the entire image into image sequences, then the model can capture global context, but structural information is ignored and convergence speed slows down
Solution Approach 1:
The patent segments the image into multiple scale levels and processes each scale separately through the Transformer, rather than treating the entire image as a single sequence. This segmentation reduces the computational burden on the Transformer while preserving global context through multi-scale aggregation.
Solution Approach 2:
The patent performs preliminary feature extraction at multiple scales using convolutional networks before feeding features into the Transformer. This preliminary processing prepares the data in a form that accelerates Transformer convergence while maintaining the ability to capture global context.
3Ease of operation
If single-scale pedestrian features are divided into feature sequences for Transformer input, then the model can process features sequentially, but multi-scale representation information is disregarded
Solution Approach 1:
The patent segments the feature extraction process into multiple scale levels, extracting pedestrian features at different scales (1/4, 1/8, 1/16) and then dividing each scale's features into sequences for Transformer input. This maintains sequential processing capability while preserving multi-scale information.
Solution Approach 2:
The patent creates a composite feature representation by combining features from multiple scales, similar to how composite materials combine different properties. The multi-scale feature sequences are aggregated to form a comprehensive pedestrian representation that retains both fine-grained and coarse-grained information.
4Device complexity
If local pedestrian features are not fully utilized in the network output, then the model structure remains simple, but crucial fine-grained information for re-identification is lost
Solution Approach 1:
The patent segments the feature extraction at multiple scales, where each scale captures different levels of detail. The 1/4 scale features preserve fine-grained local information, while 1/8 and 1/16 provide contextual support. This segmentation allows utilization of local features without requiring a fundamentally more complex model architecture.
Data Source
AI summary
The invention discloses a Transformer-based multi-scale pedestrian re-identification method. The present invention proposes a pedestrian re-identification network based on multi-scale pedestrian feature extraction and Transformer. Firstly, we designed a multi-scale feature cascade module, which aims to mine detailed feature information of pedestrians at different depths and scales, so as to obtain stronger feature representation. Secondly, we constructed a feature extraction based on Transformer to learn pedestrian features at a global scale. Finally, the features output by the Transformer are aggregated to obtain a better expression of pedestrian features, thereby improving the discrimination ability of the model. The result shows that this method has better robustness and adaptive ability, and effectively enhances the generalization ability of the model.