Automated vehicle recognition systems

1. A device, comprising:

at least one memory storing computer-executable instructions; and

at least one processor configured to access the at least one memory, wherein the at least one processor is further configured to execute the computer-executable instructions to:

determine an image;

detect a vehicle in the image;

extract at least one first feature from the image associated with the vehicle;

classify the at least one first feature from the image based at least in part on a Bayesian ratio associated with the at least one first feature from the image and at least one second feature associated with the vehicle stored in the at least one memory;

determine a match between the at least one first feature and the at least one second feature stored in the at least one memory, based at least in part on the Bayesian ratio; and

determine a ranking of the at least one first feature among a plurality of features associated with the image in a training set.

2. The device of claim 1 , wherein the image is received from a camera of a mobile device.

3. The device of claim 1 , wherein the at least one processor is further configured to execute the computer-executable instructions to:

extract the at least one first feature from the image based at least in part on a cropped image of the vehicle from the image.

4. The device of claim 1 , wherein the at least one processor is further configured to execute the computer-executable instructions to:

input the image to a convolutional neural network to detect the vehicle in the image.

5. A non-transitory computer-readable medium storing computer-executable instructions which, when executed by a processor, cause the processor to perform operations comprising:

determining an image;

detecting a vehicle in the image associated with the vehicle;

extracting at least one first feature from the image;

classifying the at least one first feature from the image based at least in part on a Bayesian ratio associated with the at least one first feature from the image and at least one second feature associated with the vehicle;

determining a match between the at least one first feature and the least one second feature stored in at least one memory; and

determining a ranking of the each of the at least one first feature.

6. The non-transitory computer-readable medium of claim 5 , wherein the image is received from a camera of a mobile device.

7. The non-transitory computer-readable medium of claim 5 , wherein the processor executes further computer-executable instructions that cause the processor to perform operations further comprising:

extracting the at least one first feature from the image based at least in part on a cropped image of the vehicle from the image.

8. The non-transitory computer-readable medium of claim 5 , wherein the processor executes further computer-executable instructions that cause the processor to perform operations further comprising:

inputting the image to a convolutional neural network to detect the vehicle in the image.

9. A device, comprising:

at least one memory storing computer-executable instructions; and

at least one processor configured to access the at least one memory, wherein the at least one processor is further configured to execute the computer-executable instructions to:

detect at least one vehicle in at least one image;

train a convolutional neural network;

extract at least one feature from the at least one image associated with the vehicle; and

classify the at least one feature from the at least one image based at least in part on a Bayesian ratio associated with the at least one first feature and at least one second feature associated with the vehicle stored in the at least one memory.

10. The device of claim 9 , wherein the at least one processor is further configured to execute the computer-executable instructions to:

extract the at least one first feature from the image based at least in part on a cropped image of the at least one vehicle in the at least one image.

11. The device of claim 9 , wherein the at least one feature is classified based at least in part on the year, make, and model of the at least one vehicle.

12. The device of claim 11 , wherein the convolutional network is trained based at least in part on a stochastic gradient descent for the year, make, and model of the at least one vehicle.

13. The device of claim 9 , wherein the at least one processor is further configured to execute the computer-executable instructions to:

detect noise in the at least one image; and

filter the noise from the at least one image.

14. A non-transitory computer-readable medium storing computer-executable instructions which, when executed by a processor, cause the processor to perform operations comprising:

detecting at least one vehicle in at least one image;

train a convolutional neural network;

extract at least one feature from the at least one image associated with the vehicle; and

classify the at least one feature from the at least one image based at least in part on a Bayesian ratio associated with the at least one first feature and at least one second feature associated with the vehicle stored in a memory.

15. The non-transitory computer-readable medium of claim 14 , wherein the processor executes further computer-executable instructions that cause the processor to perform operations further comprising:

extracting the at least one first features from the image based at least in part on a cropped image of the at least one vehicle in the at least one image.

16. The non-transitory computer-readable medium of claim 14 , wherein the at least one feature is classified based at least in part on the year, make, and model of the at least one vehicle.

17. The non-transitory computer-readable medium of claim 16 , wherein the convolutional network is trained based at least in part on a stochastic gradient descent for the year, make, and model of the at least one vehicle.

18. The non-transitory computer-readable medium of claim 14 , wherein the at least one processor is further configured to execute the computer-executable instructions to:

detect noise in the at least one image; and

filter the noise from the at least one image.