Brake light detection

Patent metadata

Field	Value
Publication number	US-10248874-B2
Application number	US-201615359158-A
Country	US
Kind code	B2
Filing date	Nov 22, 2016
Priority date	Nov 22, 2016
Publication date	Apr 2, 2019
Grant date	Apr 2, 2019

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.

First claim

What is claimed is:

1. A method comprising:
selecting a night mode or a day mode based on a pixel brightness in an image frame; detecting an image region corresponding to a vehicle based on range sensor data when in the night mode or based on camera image data when in the day mode;
converting the range sensor data into luminosity and two-color channel (LAB) color space when in the night mode;
converting the camera image data into hue, saturation, and value (HSV) color space when in the day mode;
determining at least one region of interest within the image region corresponding to a brake light of the vehicle based on the converted range sensor data or the converted camera image data;
classifying the brake light of the vehicle as on or off based on the at least one region of interest to generate a brake light classification; and executing a driving maneuver based on the brake light classification.

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

3. The method of claim 2 , wherein determining the one or more bounding boxes comprises:
extracting contours in the image 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 comprises determining based on the HSV color space when in the day mode and based on the LAB color space when in the night mode.

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

7. The method of claim 1 , wherein detecting the image region corresponding to the vehicle based on the range sensor data when in the night mode comprises:
identifying a cluster of points in the range sensor 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 , further comprising:
generating filtered frames by filtering at least one of the LAB color space or the HSV color space with different sizes based on different lighting conditions;
wherein classifying the brake light of the vehicle as on or off comprises classifying based on the filtered frames.

9. A system comprising a processor that is programmable to execute instructions stored in non-transitory computer readable storage media, the instructions comprising:
selecting a night mode or a day mode based on a pixel brightness in an image frame;
detecting an image region corresponding to a vehicle based on range sensor data when in the night mode or based on camera image data when in the day mode;
converting the range sensor data into luminosity and two-color channel (LAB) color space when in the night mode;
converting the camera image data into hue, saturation, and value (HSV) color space when in the day mode;
determining at least one region of interest within the image region corresponding to a brake light of the vehicle based on the converted range sensor data or the converted camera image data;
classifying the brake light of the vehicle as on or off based on the at least one region of interest to generate a brake light classification; and
executing a driving maneuver based on the brake light classification.

10. The system of claim 9 , wherein the instructions further comprise determining one or more bounding boxes corresponding to brake lights of the vehicle in the image region corresponding to the vehicle.

11. The system of claim 10 , 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.

12. The system of claim 10 , wherein classifying the brake light as on or off comprises classifying based on image data corresponding to the one or more bounding boxes.

13. The system of claim 10 , wherein determining the one or more bounding boxes corresponding to brake lights comprises determining based on the HSV color space when in the day mode and based on the LAB color space when in the night mode.

14. The system of claim 9 , wherein classifying the brake light of the vehicle as on or off comprises classifying using a neural network.

15. The system of claim 9 , wherein detecting the image region corresponding to the vehicle based on the range sensor data when in the night mode comprises:
identifying a cluster of points in the range sensor data corresponding to the vehicle; and
mapping a bounding box of the cluster of points to a corresponding image frame.

16. Non-transitory 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 a day mode based on a pixel brightness in an image frame;
detect an image region corresponding to a vehicle based on range sensor data when in the night mode or based on camera image data when in the day mode;
convert the range sensor data into luminosity and two-color channel (LAB) color space when in the night mode;
convert the camera image data into hue, saturation, and value (HSV) color space when in the day mode;
determine at least one region of interest within the image region corresponding to a brake light of the vehicle based on the converted range sensor data or the converted camera image data;
classify the brake light of the vehicle as on or off based on the at least one region of interest to generate a brake light classification; and
execute a driving maneuver based on the brake light classification.

17. The non-transitory 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 image region corresponding to the vehicle.

18. The non-transitory 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 image 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 non-transitory 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 non-transitory computer readable storage media of claim 16 , wherein the instructions cause the one or more processors to detect the image region corresponding to the vehicle based on the range sensor data when in the night mode by:
identifying a cluster of points in the range sensor data corresponding to the vehicle; and
mapping a bounding box of the cluster of points to a corresponding image frame.

Assignees

• Ford Global Tech Llc

Inventors

• Moosaei Maryam
• Hotson Guy
• Mahmoudieh Parsa
• Nariyambut Murali Vidya

Classifications

• G06V10/82 Primary

  using neural networks · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06V20/584

  of vehicle lights or traffic lights · CPC title

• G06T2207/30252

  Vehicle exterior; Vicinity of vehicle · CPC title

• G06T2207/10016

  Video; Image sequence · CPC title