Segmentation Embeddings for Rotation-Robust Object Tracking
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Solution Overview
Problem
Current computer vision tracking systems often lose track of objects due to misclassification when objects rotate or change appearance, or become fixated on background elements, leading to inaccuracies in applications like autonomous vehicles.
Innovation Solution
The implementation of a machine learning-based approach that segments objects in images, generates embeddings for comparison, and uses a tracking system to associate similar segmentations across multiple images, ensuring continuous tracking despite changes in appearance or background.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional tracking systems use simple object recognition methods, then the system complexity is low, but the tracking reliability deteriorates when objects rotate or change appearance
Solution Approach 1:
The patent segments objects into multiple regions or parts, comparing segmentations across frames to maintain tracking. This allows the system to handle object rotations and appearance changes by matching structural segments rather than relying on overall object appearance, thereby improving tracking reliability without requiring excessively complex global recognition systems.
Solution Approach 2:
The patent introduces embedding spaces as an additional dimensional layer for object representation. By projecting object segmentations into embedding vectors and comparing these representations, the system can reliably track objects through rotations and appearance changes while maintaining manageable system complexity through efficient vector operations.
2Measurement precision
If tracking systems continuously analyze all image data, then tracking accuracy is maintained, but computational redundancy increases
Solution Approach 1:
The patent extracts only the relevant segmentation data from full images for comparison, rather than analyzing entire images. By isolating and comparing only the segmented object regions and their embeddings, the system maintains tracking accuracy while significantly reducing computational redundancy and processing requirements.
Solution Approach 2:
The patent applies partial action by performing comparisons only on extracted segmentation embeddings rather than full image data. This selective approach maintains sufficient tracking accuracy by focusing computational resources on the critical object regions while avoiding redundant processing of background and irrelevant areas.
3Measurement precision
If tracking systems use detailed object features for identification, then object recognition accuracy improves, but the system becomes more susceptible to misclassification when objects rotate
Solution Approach 1:
The patent divides objects into segments and compares the spatial relationships and configurations of these segments rather than relying on fixed feature positions. This segmentation approach allows the system to recognize objects regardless of rotation, as the relative segment structures remain consistent even when overall object orientation changes.
Solution Approach 2:
The patent transforms object segmentations into embedding vectors that capture structural relationships in a rotation-invariant manner. By operating in this embedding dimension rather than direct pixel or feature space, the system achieves both high recognition accuracy and adaptability to rotational transformations.
Data Source
AI summary
Techniques are disclosed for tracking objects in sensor data, such as multiple images or multiple LIDAR clouds. The techniques may include comparing segmentations of sensor data such as by, for example, determining a similarity of a first segmentation of first sensor data and a second segmentation of second sensor data. Comparing the similarity may comprise determining a first embedding associated with the first segmentation and a second embedding associated with the second segmentation and determining a distance between the first embedding and the second embedding. The techniques may improve the accuracy and/or safety of systems integrating the techniques discussed herein.