Tracking objects using sensor data segmentations and/or representations
AssigneeZoox, Inc.
InventorsBryce A. Evans, Derek Xiang Ma, Sarah Tariq
Life of this patent
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Abstract
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.
Claims1
A method comprising:
- receiving, from one or more sensors, a first image comprising a representation of an object;
- receiving, as a first segmentation, a first portion of the first image associated with the object;
- inputting, into a machine learned model, the first segmentation, the machine learned model being trained to output embeddings associated with an input segmentation;
- receiving, from the machine learned model, a first embedding associated with the first segmentation;
- receiving, from the one or more sensors, a second image;
- receiving, as a second segmentation, a second portion of the second image associated with the object;
- inputting, into the machine learned model, the second segmentation;
- receiving, from the machine learned model, a second embedding associated with the second segmentation;
- determining a similarity between the first segmentation and the second segmentation;
- based at least in part on the similarity, associating at least one of the first segmentation or the second segmentation with a track of the object;
- and
- controlling an autonomous vehicle based at least in part on the track.
Dependencies are inferred from the claim text.