Object Segmentation Embeddings for Reliable Multi-Sensor Tracking
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Solution Overview
Problem
Current computer vision systems often lose track of objects due to misclassification or fixation on irrelevant features, leading to inaccuracies in applications like autonomous vehicle operation.
Innovation Solution
The use of machine learning models for object segmentation and embedding to compare and associate segmentations across multiple images, ensuring consistent tracking by determining similarity and updating object tracks based on these comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current tracking systems use simple object recognition methods, then the system complexity is low, but the tracking reliability deteriorates due to misclassification and fixation on irrelevant features
Solution Approach 1:
The patent applies segmentation by dividing the object recognition process into distinct components: generating multiple candidate segmentations of the image, creating embeddings for each segmentation, and comparing these embeddings to determine object identity. This segmentation approach allows the system to evaluate multiple potential object interpretations simultaneously, improving tracking reliability by selecting the most accurate segmentation while maintaining manageable system complexity through modular processing steps.
2Measurement precision
If the tracking system uses detailed object analysis, then the measurement precision improves, but the computation time increases due to redundant processing
Solution Approach 1:
The patent implements preliminary action by pre-computing embeddings for multiple candidate segmentations in advance, before the actual tracking decision is needed. These embeddings are stored and can be quickly retrieved and compared when tracking updates are required. This approach enables detailed object analysis with high measurement precision while reducing computation time during critical tracking moments, as the heavy processing has already been performed preliminarily.
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.