Brake Light Detection

• US 20180144202A1
• Filed: 11/22/2016
• Published: 05/24/2018
• Est. Priority Date: 11/22/2016
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Moosaei, Maryam, Hotson, Guy, Mahmoudieh, Parsa, Nariyambut Murali, Vidya
• Granted Patent: US 10,248,874 B2
• Time in Patent Office: Days
• CPC Class Codes: G06T 2207/10004 Still image; Photographic image
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/30248 Vehicle exterior or interior
• CPC Class Codes: G06T 2207/30252 Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 7/90 Determination of colour characteristics
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: G06V 20/584 of vehicle lights or traffic lights

Abstract

Systems, methods, and devices for detecting brake lights are disclosed herein. A system includes a mode component, a vehicle region component, and a classification component. The mode component is configured to select a night mode or day mode based on a pixel brightness in an image frame. The vehicle region component is configured to detect a region corresponding to a vehicle based on data from a range sensor when in a night mode or based on camera image data when in the day mode. The classification component is configured to classify a brake light of the vehicle as on or off based on image data in the region corresponding to the vehicle.

Claims

1. A method comprising:

selecting a night mode or day mode based on a pixel brightness in an image frame;

detecting a region corresponding to a vehicle based on data from a range sensor when in a night mode or based on camera image data when in the day mode; and

classifying a brake light of the vehicle as on or off based on image data in the region corresponding to the vehicle.

2. The method of claim 1, the method further comprising determining one or more bounding boxes corresponding to brake lights of the vehicle in the region corresponding to the vehicle.

3. The method of claim 2, wherein determining the one or more bounding boxes comprises;

extracting contours in the region corresponding to the vehicle; and

selecting contours having a shape or size corresponding to a brake light shape or size.

4. The method of claim 2, wherein classifying the brake light comprises classifying based on image data corresponding to the one or more bounding boxes.

5. The method of claim 2, wherein determining the one or more bounding boxes corresponding to brake lights comprise determining based on image data in a hue, saturation, and value (HSV) format for the day mode and based on image data in a luminosity, A channel, and B channel (LAB) format for the night mode.

6. The method of claim 1, wherein classifying a brake light of the vehicle as on or off comprises classifying using a neural network.

7. The method of claim 1, wherein detecting the region corresponding to a vehicle based on data from a range sensor when in the night mode comprises:

identifying a cluster of points in the range data corresponding to the vehicle; and

mapping a bounding box of the cluster of points to a corresponding image frame.

8. The method of claim 1, wherein the range data in the night mode comprises one or more of radar data, ultrasound data, and light ranging and detection (LIDAR) data.

9. A system comprising:

a mode component configured to select a night mode or day mode based on a pixel brightness in an image frame;

a vehicle region component configured to detect a region corresponding to a vehicle based on data from a range sensor when in a night mode or based on camera image data when in the day mode; and

a classification component configured to classify a brake light of the vehicle as on or off based on image data in the region corresponding to the vehicle.

10. The system of claim 9, further comprising a brake light region component configured to determine one or more bounding boxes corresponding to brake lights of the vehicle in the region corresponding to the vehicle.

11. The system of claim 10, wherein the brake light region component is configured to determine the one or more bounding boxes by:

extracting contours in the region corresponding to the vehicle; and

selecting contours having a shape or size corresponding to a brake light shape or size.

12. The system of claim 10, wherein the classification component is configured to classify the brake light as on or off by classifying based on image data corresponding to the one or more bounding boxes.

13. The system of claim 10, wherein the brake light region component is configured to determine the one or more bounding boxes corresponding to brake lights based on image data in a hue, saturation, and value (HSV) format for the day mode and based on image data in a Lab format for the night mode.

14. The system of claim 9, wherein the classification component is configured to classify the brake light of the vehicle as on or off using a neural network.

15. The system of claim 9, wherein the vehicle region component detecting the region corresponding to a vehicle based on data from a range sensor when in the night mode comprises:

identifying a cluster of points in the range data corresponding to the vehicle; and

mapping a bounding box of the cluster of points to a corresponding image frame.

16. Computer readable storage media storing instructions that, when executed by one or more processors, cause the one or more processors to:

select a night mode or day mode based on a pixel brightness in an image frame;

detect a region corresponding to a vehicle based on data from a range sensor when in a night mode or based on camera image data when in the day mode; and

classify a brake light of the vehicle as on or off based on image data in the region corresponding to the vehicle.

17. The computer readable storage media of claim 16, further comprising instructions that cause the one or more processors to determine one or more bounding boxes corresponding to brake lights of the vehicle in the region corresponding to the vehicle.

18. The computer readable storage media of claim 17, wherein the instructions cause the one or more processors to determine the one or more bounding boxes by:

extracting contours in the region corresponding to the vehicle; and

selecting contours having a shape or size corresponding to a brake light shape or size;

wherein the instructions cause the one or more processors to classify the brake light based on image data corresponding to the one or more bounding boxes.

19. The computer readable storage media of claim 16, wherein the instructions cause the one or more processors to classify the brake light of the vehicle as on or off using a neural network.

20. The computer readable storage media of claim 16, wherein the vehicle region component detecting the region corresponding to a vehicle based on data from a range sensor when in the night mode comprises:

identifying a cluster of points in the range data corresponding to the vehicle; and

mapping a bounding box of the cluster of points to a corresponding image frame.