Restraint device localization
Systems and methods are disclosed related to restraint device (e.g., seatbelt) localization. In one embodiment, the disclosure relates to systems and methods for seatbelt detection and modeling. A vehicle may be occupied by one or more occupants wearing one or more seatbelts. A camera or other sensor is placed within the vehicle to capture images of the one or more occupants. A system analyzes the images to detect and model seatbelts depicted in the images. Specifically, the system may scan the images and areas of the images that may correspond to seatbelts. The system may assemble candidate areas of the images that may correspond to seatbelts, and refine the candidate areas based on various constraints. The system may build models based on the refined candidate areas that indicate the seatbelts. The system may visualize the models indicating the seatbelts using the images.
1. A method, comprising:
receiving an image representing a subject and a restraint device corresponding to the subject;
performing an initial classification of areas of the image based at least in part on one or more groups of pixels comprising at least one edge of the restraint device;
applying a set of constraints to the areas of the image to refine the initial classification to obtain a refined classification;
generating a model indicative of an application of the restraint device based at least in part on the refined classification; and,
controlling, using one or more circuits, functionality of at least one subsystem of a vehicle based at least in part on the model.
2. The method of claim 1 , wherein the initial classification is performed by using a pixel and a plurality of neighboring pixels along a specified direction to determine a set of pixels that are part of the restraint device.
3. The method of claim 2 , wherein determining the set of pixels that are part of the restraint device is based, at least in part, on intensity levels of the pixel and the plurality of neighboring pixels along the specified direction.
4. The method of claim 1 , wherein the restraint device comprises one or more attributes including at least: a width size of the restraint device and one or more anchors for the restraint device in a vehicle.
5. The method of claim 1 , wherein the image is captured by a camera that comprises one or more configurations including at least: calibrations of the camera, camera pose, and a type of camera lens.
6. The method of claim 1 , further comprising: activating a signal to indicate that the restraint device is in an improper position relative to the subject.
7. The method of claim 1 , further comprising: using the model to determine a position of the restraint device relative to the subject, wherein the model approximates a shape of the restraint device.
8. A system, comprising:
one or more processors;
memory that stores computer-executable instructions that are executable by the one or more processors to cause the system to:
perform an initial classification of areas of an image based at least in part on one or more groups of pixels comprising at least one edge of a restraint device applied to a subject;
apply a set of constraints to the areas of the image to refine the initial classification to obtain a refined classification;
generate a model indicative of an application of the restraint device based at least in part on the refined classification; and
control, using one or more circuits, functionality of at least one subsystem of a vehicle based at least in part on the model.
9. The system of claim 8 , wherein the model comprises a polynomial curve with coefficients calculated to match a shape of the restraint device.
10. The system of claim 8 , wherein:
the system comprises a parallel processing unit (PPU);
the image comprises one or more areas; and
the one or more areas are classified using one or more threads of the PPU in parallel.
11. The system of claim 8 , wherein the model indicates if the restraint device is applied to the subject correctly.
12. The system of claim 8 , wherein the set of constraints include at least a range of widths.
13. The system of claim 8 , wherein the areas of the image correspond to a grouping of one or more pixels of the image.
14. The system of claim 8 , wherein the image is obtained from one or more networks from one or more systems of a vehicle.
15. The system of claim 14 , wherein the model of the restraint device is provided to the one or more systems of the vehicle through the one or more networks.
16. A vehicle, comprising:
a propulsion system;
an image capturing device able to capture images of at least one passenger of the vehicle; and
a computer system comprising instructions executable by the computer system to at least:
access an image representing a subject and a restraint device applied to the subject;
perform an initial classification of areas of the image as representing based at least in part on one or more groups of pixels comprising at least one edge of the restraint device;
apply a set of constraints to the areas of the image to refine the initial classification to obtain a refined classification;
generate a model indicative of an application of the restraint device based at least in part on the refined classification; and
control functionality of at least one subsystem of the vehicle based at least in part on the model.
17. The vehicle of claim 16 , wherein the restraint device is a seatbelt of the vehicle.
18. The vehicle of claim 16 , wherein the one subsystem of the vehicle is a warning system that indicates whether the restraint device is worn and applied correctly.
19. The vehicle of claim 16 , wherein the one subsystem of the vehicle sends signals to control propulsion of the vehicle through the propulsion system.
20. The vehicle of claim 16 , wherein the one subsystem of the vehicle transmits indications of whether the restraint device is worn and applied correctly to one or more remote systems.