Neural Network Classification and Re-identification Architecture
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Solution Overview
Problem
Surveillance systems face computational intensity in accurately tracking individuals and objects across multiple cameras, and the training of neural networks for re-identification is resource-intensive due to the need for both classification and re-identification processes.
Innovation Solution
A neural network architecture that performs feature extraction and provides features to both classification and re-identification layers in parallel, reducing the need for repeated feature extraction processes by overlapping these processes during the training phase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the neural network performs both classification and re-identification by searching through all images, then the accuracy of tracking targets is improved, but the computational resources and time required increase significantly
Solution Approach 1:
The patent segments the neural network into two distinct branches: a classification branch that categorizes objects into predefined classes, and a re-identification branch that performs fine-grained discrimination between similar instances. This segmentation allows each branch to specialize in its specific task, improving overall tracking accuracy while reducing the computational burden compared to a single monolithic network searching through all images.
Solution Approach 2:
The patent extracts and separates the re-identification function from the general classification process. By taking out the re-identification branch as an independent component with its own feature extraction and comparison mechanisms, the system can focus computational resources on specific discrimination tasks rather than searching through all images for every target identification.
2Device complexity
If the system uses a single neural network for both classification and re-identification, then the device complexity is reduced, but the training resource allocation becomes inefficient
Solution Approach 1:
The neural network is segmented into two parallel branches with distinct architectures optimized for their specific functions. The classification branch uses standard classification layers, while the re-identification branch employs specialized feature extraction and metric learning components. This segmentation enables independent training and optimization of each branch, improving training efficiency while maintaining manageable system complexity.
Solution Approach 2:
The patent implements a shared feature extraction backbone that serves both classification and re-identification tasks. This multi-functional base network extracts general object features that are then processed by task-specific branches, allowing the system to maintain relatively low overall complexity while enabling efficient specialized training for each function through the shared representations.
Data Source
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AI summary
Aspects of the present disclosure include methods, systems, and non-transitory computer readable media that perform the steps of receiving one or more snapshots, extracting one or more features from the one or more snapshots, and providing the one or more features to a first classification layer for classifying a first target and a second classification layer for re-identifying a second target.