US 2023/0069482 A1 · MACHINE LEARNING-BASED ENVIRONMENT FAIL-SAFES THROUGH MULTIPLE CAMERA VIEWS

Abstract

A computing system may include a fail-safe learning engine configured to access camera data captured by multiple cameras positioned within an environment during a learning phase, generate training data based on the camera data captured by the multiple cameras, and construct a human detection model using the training data. The computing system may also include a fail-safe trigger engine configured to access camera data captured by the multiple cameras positioned within the environment during an active phase, and the camera data captured during the active phase may include a target object. The fail-trigger engine may further be configured to provide, as an input to the human detection model, the camera data that includes the target object and execute a fail-safe action in the environment responsive to the determination, provided by the human detection model, indicating that the target object is a human.

Claims20

Dependent claims

A system comprising:

a fail-safe learning engine configured to:
access camera data captured by multiple cameras positioned within an environment during a learning phase;
generate training data based on the camera data captured by the multiple cameras;
and
construct a human detection model using the training data, the human detection model configured to provide a determination indicative of whether a given object within a view of a camera in the environment is a human or not;
and
a fail-safe trigger engine configured to:
access camera data captured by the multiple cameras positioned within the environment during an active phase, wherein the camera data captured during the active phase includes a target object;
provide, as an input to the human detection model, the camera data that includes the target object;
and
execute a fail-safe action in the environment responsive to the determination, provided by the human detection model, indicating that the target object is a human.

8 more elements

A method comprising:

by a computing system:
accessing camera data captured by multiple cameras positioned within an environment during an active phase, wherein the camera data captured during the active phase includes a target object;
providing the camera data that includes the target object as an input to a human detection model, wherein the human detection model is configured to provide a determination indicative of whether a given object within a view of a camera in the environment is a human or not;
and
executing a fail-safe action in the environment responsive to the determination, provided by the human detection model, indicating that the target object is a human.

2 more elements

A non-transitory machine-readable medium comprising instructions that, when executed by a processor, cause a computing system to:

access camera data captured by multiple cameras positioned within an environment during an active phase, wherein the camera data captured during the active phase includes a target object;
provide, as an input to a human detection model, the camera data that includes the target object, wherein the human detection model is configured to provide a determination indicative of whether a given object within a view of a camera in the environment is a human or not;
and
execute a fail-safe action in the environment responsive to the determination, provided by the human detection model, indicating that the target object is a human.

1 more element

Dependencies are inferred from the claim text.

Machine learning-based environment fail-safes through multiple camera views

Life of this patent

Machine learning-based environment fail-safes through multiple camera views

Life of this patent

Abstract

Claims20