Neural Network Object Re-Identification With Shared Detection Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing object detection and re-identification methods using external tracking algorithms are error-prone when objects are close together or partially concealed, as they rely on simple properties and require significant computational resources for feature extraction.
Innovation Solution
A neural network-based method that combines anchor-based object detection with embedding data to accurately re-identify objects across different frames and camera views by extracting features, determining classification, regression, and embedding data, which simplifies tracking and reduces computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If external tracking algorithms are used for object detection and re-identification, then object tracking can be performed, but detection accuracy deteriorates when objects are close together or partially concealed
Solution Approach 1:
The patent combines object detection and re-identification into a unified neural network framework. The detector and re-identification network share feature extraction capabilities, allowing the system to simultaneously perform both functions with improved accuracy. The loss function integrates both detection loss and re-identification loss, enabling joint optimization of both tasks.
Solution Approach 2:
The neural network is designed to perform multiple functions: object detection, feature extraction, and re-identification. By using shared convolutional layers for feature extraction that serve both detection and re-identification purposes, the system achieves multi-functionality without requiring separate processing pipelines.
2Productivity
If traditional tracking algorithms are used, then computational resources can be managed, but detection accuracy worsens for concealed or close objects
Solution Approach 1:
The patent extracts discriminative features specifically for re-identification purposes using dedicated fully connected layers that process the extracted features. This separation allows the system to maintain computational efficiency while improving detection accuracy by focusing on specific feature representations that are most useful for distinguishing similar objects.
3Productivity
If simple object properties are used for tracking, then computational costs are reduced, but re-identification accuracy deteriorates
Solution Approach 1:
The patent applies local quality by using different processing stages for different purposes: early convolutional layers extract general features efficiently, while later specialized layers (fully connected layers) process these features to create discriminative re-identification descriptors. This allows computational resources to be allocated efficiently at different stages of the pipeline.
Solution Approach 2:
The system uses composite feature representations that combine general object features from the detection network with discriminative features specifically extracted for re-identification. This composite approach allows the system to maintain low computational cost while achieving high re-identification accuracy through the combination of different feature types.
Data Source
AI summary
A method for detecting and re-identifying objects using a neural network. The method includes the steps: extracting features from an image, the features comprising information about at least one object in the image; detecting the at least one object in the image using an anchor-based object detection based on the extracted features, classification data being determined by a classification for detecting the object with the aid of at least one anchor and regression data being determined by a regression; and re-identifying the at least one object by determining embedding data based on the extracted features, the embedding data representing an object description for the at least one feature of the image.