Neural Network Object Descriptors for Multi-View Tracking
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Solution Overview
Problem
Current machine learning techniques for object recognition in images and videos are limited in their ability to identify objects from multiple views and track them over time or across different instances of image or video data in real-time settings, particularly in applications like robotics where specific object identification is crucial.
Innovation Solution
The use of a detection transformer and deformable attention modules within a neural network architecture that generates unique descriptors for objects, allowing for identification and tracking across different views and orientations, with supervised contrastive learning to ensure descriptor consistency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is trained using images of specific objects from multiple views, then object recognition accuracy is improved, but training data requirements and system complexity increase
Solution Approach 1:
The patent uses a neural network to generate synthetic object descriptors that copy and represent the essential features of objects without requiring actual training images. The network learns to create descriptors that capture object identity and appearance characteristics, enabling recognition without extensive real-world training data
Solution Approach 2:
The patent replaces the traditional mechanical approach of collecting and processing large volumes of actual object images with a neural network-based system that generates descriptors computationally. This substitution eliminates the need for physical training data collection while maintaining recognition accuracy
2Measurement precision
If machine learning techniques are used to segment and identify objects, then object identification capability is improved, but real-time tracking ability deteriorates
Solution Approach 1:
The patent changes the fundamental parameter from processing entire images to processing compact object descriptors. By representing objects as concise feature vectors rather than full images, the system achieves both accurate identification and efficient real-time tracking, as descriptor comparison is computationally much lighter than image processing
Solution Approach 2:
The patent extracts only the essential identifying features of objects into compact descriptors, separating the identification function from the full image data. This extraction enables rapid comparison and tracking without processing redundant visual information
3Reliability
If neural networks generate unique descriptors for objects, then object tracking across different views is improved, but computational resources required increase
Solution Approach 1:
The patent applies partial action by generating only the essential descriptor components needed for identification and tracking, rather than processing complete object representations. The neural network focuses on extracting and generating only the critical features that maintain tracking consistency across views, reducing unnecessary computational overhead
Data Source
AI summary
Apparatuses, systems, and techniques are presented to identify one or more objects. In at least one embodiment, one or more neural networks can be used to identify one or more objects based, at least in part, on one or more descriptors of one or more segments of the one or more objects.