Vehicle identification system and method

• US 10,783,374 B2
• Filed: 01/10/2020
• Issued: 09/22/2020
• Est. Priority Date: 01/11/2019
• Status: Active Grant

• Current Assignee: Motor Trend Group, LLC (Warner Bros. Discovery, Inc.)
• Original Assignee: Motor Trend Group, LLC (Warner Bros. Discovery, Inc.)
• Inventors: Wellen, Alex, Chiavacci, Pepper
• Primary Examiner(s): Allison, Andrae S
• Application Number:
  US16/740,364
• Publication Number:
  US 20200226381A1
• Time in Patent Office: 256 Days
• Field of Search: None
• CPC Class Codes: A63F 13/213 comprising photodetecting means, e.g. cameras, photodiodes or infrared cells A63F13/219, A63F13/655 take precedence
• CPC Class Codes: A63F 13/65 automatically by game devices or servers from real world data, e.g. measurement in live racing competition
• CPC Class Codes: A63F 13/69 by enabling or updating specific game elements, e.g. unlocking hidden features, items, levels or versions
• CPC Class Codes: A63F 13/80 Special adaptations for executing a specific game genre or game mode
• CPC Class Codes: A63F 13/803 Driving vehicles or craft, e.g. cars, airplanes, ships, robots or tanks
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06V 10/454 Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: G06V 20/20 in augmented reality scenes
• CPC Class Codes: G06V 20/54 of traffic, e.g. cars on the road, trains or boats
• CPC Class Codes: G06V 2201/08 Detecting or categorising vehicles
• CPC Class Codes: G07F 17/3253 involving articles, e.g. paying in bottles, paying out toys
• CPC Class Codes: G07F 17/3286 Type of games

Abstract

The system and method are configured to identify cars and obtain information about the identified cars. The system and method involves a software application installed on a computing device that includes a camera configured to capture images. After the software application is installed, the user can activate the camera through the software application and point the camera at a vehicle. The software application includes an object recognition function that can recognize the vehicle in the view of the camera. After recognizing the vehicle, the software application displays information about vehicle on a screen of the computing device. The software application can be used with an event such as an auto show. In addition to identifying the cars, the identified information can be used for other purposes such as a part of a scavenger hunt or other contests. Awards can be given to participants who complete the contest.

Claims

1. A computer-implemented method for identifying a vehicle comprising:

implementing a software application on a mobile device configured to identify vehicles in an auto show event and to obtain information about vehicles and unlock prizes, wherein execution of the software application allows a processor and memory of the mobile device to;

implement a first database including target vehicles in the auto show event;

implement a second database including prize information;

activate a camera of the mobile device;

identify a vehicle in the view of the camera, wherein the step of identifying includes recognizing that the vehicle in the view matches or closely matches a target vehicle stored in the first database and displaying vehicle information of the target vehicle when the identified vehicle matches or closely matches the target vehicle on a screen of the mobile device;

identify the target vehicle to which the vehicle in the view of the camera matches or closely matches as an unlocked vehicle and track a number of unlocked vehicles; and

present prize information from the second database on the screen of the mobile device according to the number of unlocked vehicles.

2. The method of claim 1, wherein information of each of the target vehicles stored in the first database is inaccessible to user of the software application before the target vehicle is identified by the software application as an unlocked vehicle.

3. The method of claim 1, wherein the execution of the software application allows a processor and memory of the mobile device to further implement a control interface configured to control user access to the information of each of the target vehicles stored in the first database.

4. The method of claim 3, wherein the control interface is configured to require user of the software application to perform additional interactions with the software application after the software application recognizing that the vehicle in the view of the camera matches or closely matches a target vehicle stored in the first database in order to identify the target vehicle as unlocked vehicle.

5. The method of claim 1, wherein the execution of the software application allows a processor and memory of the mobile device to further compare the number of unlocked vehicles with another number of unlocked vehicles tracked by the software application implemented on another mobile device.

6. The method of claim 5, wherein the execution of the software application allows a processor and memory of the mobile device to further present prize information from the second database on the screen of the mobile device based on the step of comparison.

7. The method of claim 1, wherein the execution of the software application allows a processor and memory of the mobile device to further present prize information from the second database on the screen of the mobile device when a specific target vehicle is identified as unlocked.

8. The method of claim 1, wherein the step of identifying a vehicle is executed by a machine learning machine, the machine learning machine includes:

a detection component of a pre-trained neural network, wherein the detection component is configured to detect patterns, edges, shapes, and other characteristics of an object in a set of images containing the object, wherein the object is not a vehicle; and

a neural network configured to receive another set of images that each contains a vehicle and relied on the detection component to learn that each image contains the vehicle.

9. The method of claim 1, wherein the step of identifying a vehicle is executed by a machine learning machine, the machine learning machine includes

a first artificial neural network configured to receive the frame, detect features of the object in the frame, and produce a features vector representative of the detected features;

and

a second artificial neural network configured to receive the features vector and classify the features vector to one of the category labels implemented on the second artificial neural network;

wherein the machine learning system is configured to identify the object in the frame based on the category label to which the features vector is classified.

10. The method of claim 1, wherein the step of identifying a vehicle is executed by a machine learning machine, the machine learning machine includes:

a first artificial neural network configured to receive the frame, detect features of the vehicle in the frame, and produce a features vector representative of the detected features, wherein the first artificial neural network is a feature learning neural network from a general task artificial neural network that has been trained with images from a large dataset, wherein the images from the large dataset include no images of the target vehicles; and

a second artificial neural network configured to receive the features vector and classify the features vector to one of the category labels implemented on the second artificial neural network, wherein the second artificial neural network is an image classification neural network from a custom neural network and the image classification neural network has been trained using the feature learning neural network from a general task artificial neural network and with images from a small dataset, wherein the images from the small dataset include images of the target vehicles.

11. A non-transitory computer readable medium storing computer-executable instructions that causes a computer to execute a method, the method comprising:

implementing a software application on a mobile device configured to identify vehicles in an auto show event and to obtain information about vehicles and unlock prizes, wherein execution of the software application allows a processor and memory of the mobile device to;

implement a first database including target vehicles in the auto show event;

implement a second database including prize information;

activate a camera of the mobile device;

identify a vehicle in the view of the camera, wherein the step of identifying includes recognizing that the vehicle in the view matches or closely matches a target vehicle stored in the first database and displaying vehicle information of the target vehicle when the identified vehicle matches or closely matches the target vehicle on a screen of the mobile device;

identify the target vehicle to which the vehicle in the view of the camera matches or closely matches as an unlocked vehicle and track a number of unlocked vehicles; and

present prize information from the second database on the screen of the mobile device according to the number of unlocked vehicles.

12. The medium of claim 11, wherein information of each of the target vehicles stored in the first database is inaccessible to user of the software application before the target vehicle is identified by the software application as an unlocked vehicle.

13. The medium of claim 11, wherein the execution of the software application allows a processor and memory of the mobile device to further implement a control interface configured to control user access to the information of each of the target vehicles stored in the first database.

14. The medium of claim 13, wherein the control interface is configured to require user of the software application to perform additional interactions with the software application after the software application recognizing that the vehicle in the view of the camera matches or closely matches a target vehicle stored in the first database in order to identify the target vehicle as unlocked vehicle.

15. The medium of claim 11, wherein the execution of the software application allows a processor and memory of the mobile device to further compare the number of unlocked vehicles with another number of unlocked vehicles tracked by the software application implemented on another mobile device.

16. The medium of claim 15, wherein the execution of the software application allows a processor and memory of the mobile device to further present prize information from the second database on the screen of the mobile device based on the step of comparison.

17. The medium of claim 11, wherein the execution of the software application allows a processor and memory of the mobile device to further present prize information from the second database on the screen of the mobile device when a specific target vehicle is identified as unlocked.

18. The medium of claim 11, wherein the step of identifying a vehicle is executed by a machine learning machine, the machine learning machine includes:

a detection component of a pre-trained neural network, wherein the detection component is configured to detect patterns, edges, shapes, and other characteristics of an object in a set of images containing the object, wherein the object is not a vehicle; and

a neural network configured to receive another set of images that each contains a vehicle and relied on the detection component to learn that each image contains the vehicle.

19. The medium of claim 11, wherein the step of identifying a vehicle is executed by a machine learning machine, the machine learning machine includes:

a first artificial neural network configured to receive the frame, detect features of the object in the frame, and produce a features vector representative of the detected features; and

a second artificial neural network configured to receive the features vector and classify the features vector to one of the category labels implemented on the second artificial neural network;

wherein the machine learning system is configured to identify the object in the frame based on the category label to which the features vector is classified.

20. The medium of claim 11, wherein the step of identifying a vehicle is executed by a machine learning machine, the machine learning machine includes:

a first artificial neural network configured to receive the frame, detect features of the vehicle in the frame, and produce a features vector representative of the detected features, wherein the first artificial neural network is a feature learning neural network from a general task artificial neural network that has been trained with images from a large dataset, wherein the images from the large dataset include no images of the target vehicles; and

a second artificial neural network configured to receive the features vector and classify the features vector to one of the category labels implemented on the second artificial neural network, wherein the second artificial neural network is an image classification neural network from a custom neural network and the image classification neural network has been trained using the feature learning neural network from a general task artificial neural network and with images from a small dataset, wherein the images from the small dataset include images of the target vehicles.