Bounding box embedding for object identifying
AssigneeZoox, Inc.
InventorSarah Tariq
Life of this patent
Estimating the term…
Abstract
Techniques are disclosed for implementing a neural network that outputs embeddings. Furthermore, techniques are disclosed for using sensor data to train a neural network to learn such embeddings. In some examples, the neural network may be trained to learn embeddings for instance segmentation of an object based on an embedding for a bounding box associated with the object being trained to match pixel embeddings for pixels associated with the object. The embeddings may be used for object identification, object matching, object classification, and/or object tracking in various examples.
Claims20
A system comprising:
- one or more processors;
- and
- one or more computer-readable media storing instructions executable by the one or more processors, wherein the instructions, when executed, cause the system to perform operations comprising:
- outputting, by a machine learned (ML) model trained based at least in part on a difference between a first multi-dimensional vector associated with a pixel and a second multi-dimensional vector associated with a bounding box, a segmentation associated with an object represented in an image;
- performing object detection of the object, based at least in part on the segmentation;
- and
- controlling an autonomous vehicle based at least in part on the object detection.
A computer-implemented method comprising:
- outputting, by a machine learned (ML) model trained based at least in part on a difference between a first multi-dimensional vector associated with a pixel and a second multi-dimensional vector associated with a bounding box, a segmentation associated with an object represented in an image;
- performing object detection of the object, based at least in part on the segmentation;
- and
- controlling an autonomous vehicle based at least in part on the object detection.
One or more non-transitory computer-readable media storing instructions executable by one or more processors, wherein the instructions, when executed, cause the one or more processors to perform operations comprising:
- outputting, by a machine learned (ML) model trained based at least in part on a difference between a first multi-dimensional vector associated with a pixel and a second multi-dimensional vector associated with a bounding box, a segmentation associated with an object represented in an image;
- performing object detection of the object, based at least in part on the segmentation;
- and
- controlling an autonomous vehicle based at least in part on the object detection.
Dependencies are inferred from the claim text.