Facial recognition technology for improving motor carrier regulatory compliance
Methods for improving compliance with regulations pertaining to vehicle driving records are disclosed. One or more digital images from a camera mounted in a vehicle are received. Based on a determination that the vehicle has hours of service that have not been assigned to a driver, a subset of the one or more digital images corresponding to the hours of service are identified based on the timestamps. The subset of the one or more digital images are processed to identify a correspondence between a face of a person included in the one or more digital images and a face of a known person. Based on the correspondence transgressing a threshold level of correspondence, a user interface is generated for presentation on a device. The user interface includes an interactive user interface element for accepting a recommendation to assign the known person as the driver for the unassigned hours of service.
1. A system comprising:
one or more computer processors;
one or more computer memories;
a set of instructions stored in the one or more computer memories, the set of instructions configuring the one or more computer processors to perform operations, the operations comprising:
receiving one or more digital images from a camera mounted on a vehicle, wherein the camera is configured to capture the one or more digital images when the vehicle begins moving after being stopped for a threshold duration;
processing the one or more digital images to detect one or more faces within the one or more digital images, wherein the processing of the one or more digital images includes identifying a correspondence between a face of a person included in the one or more digital images and a face of a known person based on the correspondence transgressing a threshold level of correspondence;
comparing the one or more detected faces to stored images of authorized drivers of the vehicle to identify a match between the one or more detected faces and one of the stored images based on a similarity score exceeding a threshold; and
assigning unassigned driving hours to an authorized driver corresponding to the matched one of the stored images.
2. The system of claim 1 , the operations further comprising feeding back input of an administrator accepting or rejecting one or more recommendations to assign known people as drivers into a machine-learning system.
3. The system of claim 1 , wherein the camera records a view of an interior of the vehicle.
4. The system of claim 1 , wherein the processing of the one or more digital images includes generating a depth model based on monocular image data through a stereoscopic inference model trained to construct a 3-dimensional (3D) depth model based on monocular image data.
5. The system of claim 1 , wherein the processing of the one or more digital images includes detecting features within sensor data corresponding to certain types of objects that correspond with an event definition.
6. The system of claim 1 , wherein the processing of the one or more digital images includes applying a neural network trained to recognize features or signals corresponding to certain events or precursors to events.
7. A method comprising:
receiving one or more digital images from a camera mounted on a vehicle, wherein the camera is configured to capture the one or more digital images when the vehicle begins moving after being stopped for a threshold duration;
processing the one or more digital images to detect one or more faces within the one or more digital images, wherein the processing of the one or more digital images includes identifying a correspondence between a face of a person included in the one or more digital images and a face of a known person based on the correspondence transgressing a threshold level of correspondence;
comparing the one or more detected faces to stored images of authorized drivers of the vehicle to identify a match between the one or more detected faces and one of the stored images based on a similarity score exceeding a threshold; and
assigning unassigned driving hours to an authorized driver corresponding to the matched one of the stored images.
8. The method of claim 7 , further comprising feeding back input of an administrator accepting or rejecting one or more recommendations to assign known people as drivers into a machine-learning system.
9. The method of claim 7 , wherein the camera records a view of an interior of the vehicle.
10. The method of claim 7 , wherein the processing of the one or more digital images includes generating a depth model based on monocular image data through a stereoscopic inference model trained to construct a 3-dimensional (3D) depth model based on monocular image data.
11. The method of claim 7 , wherein the processing of the one or more digital images includes detecting features within sensor data corresponding to certain types of objects that correspond with an event definition.
12. The method of claim 7 , wherein the processing of the one or more digital images includes applying a neural network trained to recognize features or signals corresponding to certain events or precursors to events.
13. A non-transitory computer-readable storage medium storing a set of instructions that, when executed by one or more computer processors, causes the one or more computer processors to perform operations, the operations comprising:
receiving one or more digital images from a camera mounted on a vehicle, wherein the camera is configured to capture the one or more digital images when the vehicle begins moving after being stopped for a threshold duration;
processing the one or more digital images to detect one or more faces within the one or more digital images, wherein the processing of the one or more digital images includes identifying a correspondence between a face of a person included in the one or more digital images and a face of a known person based on the correspondence transgressing a threshold level of correspondence;
comparing the one or more detected faces to stored images of authorized drivers of the vehicle to identify a match between the one or more detected faces and one of the stored images based on a similarity score exceeding a threshold; and
assigning unassigned driving hours to an authorized driver corresponding to the matched one of the stored images.
14. The non-transitory computer-readable storage medium of claim 13 , the operations further comprising feeding back input of an administrator accepting or rejecting one or more recommendations to assign known people as drivers into a machine-learning system.
15. The non-transitory computer-readable storage medium of claim 13 , wherein the camera records a view of an interior of the vehicle.
16. The non-transitory computer-readable storage medium of claim 13 , wherein the processing of the one or more digital images includes generating a depth model based on monocular image data through a stereoscopic inference model trained to construct a 3-dimensional (3D) depth model based on monocular image data.
17. The non-transitory computer-readable storage medium of claim 13 , wherein the processing of the one or more digital images includes detecting features within sensor data corresponding to certain types of objects that correspond with an event definition.