Shared Attribute Localization for Faster Pedestrian Recognition
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Solution Overview
Problem
Existing pedestrian attribute recognition algorithms based on deep learning suffer from low accuracy and long running times due to the need for multiple attribute localization modules (ALMs) for each attribute, leading to increased computational load.
Innovation Solution
A pedestrian attribute recognition system with at least one attribute localization module (ALM) that corresponds to multiple pedestrian attributes, utilizing spatial transformation and channel attention units to extract and calibrate feature information, reducing the number of ALMs required and improving recognition efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple attribute localization modules (ALMs) are used for each pedestrian attribute, then recognition accuracy is improved, but computational load increases and running time increases
Solution Approach 1:
The patent merges multiple attribute localization modules into a single shared ALM that can process multiple pedestrian attributes simultaneously. The spatial transformation unit and channel attention unit are shared across different attributes, reducing redundant computations while maintaining recognition accuracy through multi-label classification mechanisms.
Solution Approach 2:
The attribute localization module is designed with universal functionality to handle multiple pedestrian attributes through a single unified structure. The spatial transformation and channel attention mechanisms serve multiple attributes concurrently, making the system more efficient without sacrificing the specialized recognition capability for each attribute.
2Measurement precision
If multiple attribute localization modules (ALMs) are used for each pedestrian attribute, then recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple separate attribute localization modules into one integrated module that handles multiple attributes. This merging reduces the overall number of components in the system, simplifying the architecture while preserving the capability to recognize multiple attributes through shared spatial transformation and channel attention units.
Solution Approach 2:
The unified attribute localization module is designed to be universal, capable of processing multiple pedestrian attributes through a single structure. This multi-functional design reduces system complexity by eliminating the need for separate specialized modules for each attribute, while maintaining accurate recognition through sophisticated feature extraction mechanisms.
Data Source
AI summary
A pedestrian attribute recognition method based on a pedestrian attribute recognition system is provided, and the system includes at least one attribute localization module. Each attribute localization module corresponds to a plurality of pedestrian attributes; and the attribute localization module includes a spatial transformation unit and an attribute recognition unit. The method includes: extracting, by the spatial transformation unit, feature information in a discriminable region from feature information input into the spatial transformation unit, the discriminable region being related to the plurality of pedestrian attributes corresponding to the attribute localization module; and outputting, by the attribute recognition unit, recognition results of the plurality of pedestrian attributes corresponding to the attribute localization module according to the feature information in the discriminable region.