Granted Patent B2

US 12,243,270 · App. 17/493,795 · Granted Mar 4, 2025

Inventors: Gianni Rosas-Maxemin (Sacramento, CA); George Azzi (Sacramento, CA); Callam Poynter (Sacramento, CA); Robert Mazzola (Sacramento, CA)

Assignee: PIED PARKER, INC.

G06T7/80G06T7/70G06V20/52H04N23/64H04N23/661H04N23/80G06T2207/30264

Abstract

Claims (56)

1. A method of operating a camera-based parking management system, comprising:

activating an AI-enabled camera covering a staging area responsive to detecting a vehicle;

capturing a first snapshot of the staging area using the AI-enabled camera;

transmitting the first snapshot to an edge processor coupled to the AI-enabled camera to identify one or more objects shown in the first snapshot;

determining that first object observed in the first snapshot is not capable of being identified with a predefined degree of certainty based on a machine learning model;

responsive to the determining:

identifying first one or more characteristics about the environmental conditions surrounding the first object that potentially reduce an identification accuracy of one or more vehicle-specific parameters; and

identifying second one or more characteristics about the first snapshot that potentially reduce the identification accuracy of one or more vehicle-specific parameters; and

automatically adjusting one or more settings of the AI-enabled camera to capture a second snapshot based on identifying the first one or more characteristics about the environmental conditions surrounding the first object;

automatically adjusting one or more settings of the processing of images captured by the AI-enabled camera based on identifying the second one or more characteristics about the first snapshot;

wherein the second one or more characteristics about the first snapshot include a resolution of the snapshot, network availability based on the time of day, and a network speed of a network connection between the AI-enabled camera and the edge processor;

capturing the second snapshot of the staging area using the AI-enabled camera;

determining the first object observed in the first snapshot is capable of being identified with the predefined degree of certainty based on the machine learning model; and

adjusting the machine learning model based on the adjustment of the settings of the AI-enabled camera and settings of the processing of images captured by the AI-enabled camera.

2. The method of claim 1 , further comprising identifying the first object with the predefined degree of certainty based on the first snapshot and the second snapshot.

3. The method of claim 1 , wherein the first snapshot is an image, an audio clip, or a video clip.

4. The method of claim 1 , wherein the first one or more characteristics about the environmental conditions surrounding the first object include movement of the first object, an angle of the first object to the AI-enabled camera, an orientation of the AI-enabled camera, frames per second of the AI-enabled camera, and color contrast of the first object.

5. The method of claim 1 , wherein the automatically adjusting the one or more settings of the AI-enabled camera to capture the second snapshot includes: caching of one or more snapshots during a slow period of network throughput at the AI-enabled camera before sending the one or more snapshots to the edge processor.

6. A parking management system comprising:

an AI-enabled camera,

an edge computing processor, and

a set of executable computer instructions, which when executed, causes the parking management system to:

activate the AI-enabled camera covering a staging area responsive to detecting a vehicle;

capture a first snapshot of the staging area using the AI-enabled camera;

transmit the first snapshot to the edge computing processor to identify one or more objects shown in the first snapshot;

determine that a first object shown in the first snapshot is not capable of being identified with a predefined degree of certainty based on a machine learning model;

responsive to the determining:

identify first one or more characteristics about the environmental conditions surrounding the first object that potentially reduce an identification accuracy of one or more vehicle-specific parameters; and

identify second one or more characteristics about the first snapshot that potentially reduce the identification accuracy of one or more vehicle-specific parameters; and

automatically adjust one or more settings of the AI-enabled camera to capture a second snapshot based on identifying the first one or more characteristics about the environmental conditions surrounding the first object;

automatically adjust one or more settings of the processing of images captured by the AI-enabled camera based on identifying the second one or more characteristics about the first snapshot;

wherein the second one or more characteristics about the first snapshot include a resolution of the snapshot, network availability based on the time of day, and a network speed of a network connection between the AI-enabled camera and the edge computing processor;

capture the second snapshot of the staging area using the AI-enabled camera;

determine the first object observed in the first snapshot is capable of being identified with the predefined degree of certainty based on the machine learning model; and

adjust the machine learning model based on the adjustment of the settings of the AI-enabled camera and settings of the processing of images captured by the AI-enabled camera.

7. The parking management system of claim 6 , further comprising instructions, which when executed, causes the parking management system to: identify the first object with the predefined degree of certainty based on the first snapshot and the second snapshot.

8. The parking management system of claim 6 , wherein the first snapshot is an image, an audio clip, or a video clip.

9. The parking management system of claim 6 , wherein the first one or more characteristics about the environmental conditions surrounding the first object include movement of the first object, an angle of the first object to the AI-enabled camera, an orientation of the AI-enabled camera, frames per second of the AI-enabled camera, and color contrast of the first object.

10. The parking management system of claim 6 , wherein the automatically adjusting the one or more settings of the AI-enabled camera to capture the second snapshot includes: caching of one or more snapshots during a slow period of network throughput at the AI-enabled camera before sending the one or more snapshots to the edge computing processor.

11. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing system with one or more processors, cause the computing system to:

activate an AI-enabled camera covering a staging area responsive to detecting a vehicle;

capture a first snapshot of the staging area using the AI-enabled camera;

transmit the first snapshot to an edge computing processor to identify one or more objects shown in the first snapshot;

determine that the first object shown in the first snapshot is not capable of being identified with a predefined degree of certainty based on a machine learning model;

responsive to the determining:

identify first one or more characteristics about the environmental conditions surrounding the first object that potentially reduce an identification accuracy of one or more vehicle-specific parameters; and

identify second one or more characteristics about the first snapshot that potentially reduce the identification accuracy of one or more vehicle-specific parameters; and

automatically adjust one or more settings of the AI-enabled camera to capture a second snapshot to focus on a license plate of the vehicle based on identifying the first one or more characteristics about the environmental conditions surrounding the first object;

automatically adjust one or more settings of the processing of images captured by the AI-enabled camera based on identifying the second one or more characteristics about the first snapshot,

wherein the second one or more characteristics about the first snapshot include a resolution of the snapshot, network availability based on the time of day, and a network speed of a network connection between the AI-enabled camera and the edge computing processor;

capturing the second snapshot of the staging area using the AI-enabled camera;

determining the first object observed in the first snapshot is capable of being identified with the predefined degree of certainty based on the machine learning model; and

adjusting the machine learning model based on the adjustment of the settings of the AI-enabled camera and settings of the processing of images captured by the AI-enabled camera.

12. The non-transitory computer-readable storage medium of claim 11 , further comprising instructions, which when executed, cause the one or more processors to: identify the first object with the predefined degree of certainty based on the first snapshot and the second snapshot.

13. The non-transitory computer-readable storage medium of claim 11 , wherein the first snapshot is an image, an audio clip, or a video clip.

14. The non-transitory computer-readable storage medium of claim 11 , wherein the first one or more characteristics about the environmental conditions surrounding the first object include movement of the first object, an angle of the first object to the AI-enabled camera, an orientation of the AI-enabled camera, frames per second of the AI-enabled camera, and color contrast of the first object.

Assignments (1)

Continuity (2)

References Cited (25)