Object Tracking With Relational Graph Embeddings for Trajectory Prediction
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Solution Overview
Problem
Existing object tracking methods struggle to effectively handle complex dynamics of multiple objects, particularly in surveillance and autonomous driving scenarios, due to insufficient consideration of spatial-temporal relations and interactions.
Innovation Solution
A method and device that utilize relational graph embeddings to capture spatial-temporal relations between objects, incorporating a regression model to improve position prediction by considering the history of object interactions and spatial proximity, using learnable parameters trained on labeled datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object tracking methods are used, then the system is simpler to implement, but the accuracy of position prediction deteriorates due to insufficient consideration of spatial-temporal relations
Solution Approach 1:
The tracking system is segmented into distinct functional modules: an object detector for identifying objects in images, a regression model for position prediction, and a relational graph module for capturing spatial-temporal relations. Each module processes specific aspects of tracking independently, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces relational graph embeddings that add a new dimensional layer to traditional tracking. Instead of only tracking object positions in 2D image space, the system embeds objects in a relational graph space that captures spatial-temporal relationships across multiple time steps, enabling more accurate position prediction through enhanced feature representation.
2Measurement precision
If relational graph embeddings are used to capture spatial-temporal relations, then position prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The system pre-computes relational graph embeddings and stores them in a database before actual tracking operations. These pre-computed embeddings capture spatial-temporal relationships in advance, allowing the regression model to quickly retrieve and utilize this information during real-time tracking without performing complex computations on-the-fly, thus reducing computational complexity while maintaining accuracy.
Solution Approach 2:
Instead of computing relational graphs from scratch for each tracking task, the system creates and stores template relational graph embeddings that can be copied and reused across multiple tracking scenarios. This copying approach significantly reduces computational complexity by avoiding redundant calculations while preserving the accuracy benefits of relational graph analysis.
3Reliability
If the regression model uses relational graph embeddings, then trajectory prediction improves, but the time required for processing increases
Solution Approach 1:
Relational graph embeddings are pre-computed and stored in a database before actual tracking operations. This preliminary action allows the regression model to quickly retrieve pre-processed spatial-temporal relationship information during real-time tracking, significantly reducing processing time while maintaining trajectory prediction reliability.
Solution Approach 2:
The system implements a self-service mechanism where the relational graph module automatically generates and stores embeddings that the regression model can independently retrieve and utilize. This self-service architecture eliminates the need for complex real-time coordination between modules, reducing processing overhead and improving efficiency while maintaining reliable trajectory predictions.
Data Source
AI summary
A device and computer-implemented method for object tracking. The method comprises providing a sequence of digital images, determining a sequence of relational graph embeddings, wherein a first relational graph embedding of the sequence comprises a first object embedding representing a first object in a first digital image of the sequence of digital images, wherein the first relational graph embedding comprises a first relation embedding of a relation for the first object embedding, wherein the first relation embedding relates the first object embedding to embeddings representing other objects of the first digital image in the first relational graph embedding and to embeddings in a second relational graph embedding of the sequence that represent objects of a second digital image of the sequence of digital images.