Abstract: A system for processing an image including a vehicle using machine learning can include a processor in communication with a client device, and a storage medium storing instructions that, when executed, cause the processor to perform operations including: receiving an image of a vehicle from the client device; extracting one or more features from the image; based on the extracted features and using a machine learning algorithm, determining a make and a model of the vehicle; obtaining user information relating to a financing request for the vehicle; determining a real-time quote for the vehicle based on the make, the model, and the user information; and transmitting the real-time quote for display on the client device.

Abstract: The present disclosure relates to interactive projection systems. In one implementation, an interactive projection system for a vehicle window may include a film having a plurality of sensors embedded therein, the plurality of sensors configured to detect local pressure disturbance on the film; a projector configured to project a user interface onto the film; and at least one processor. The at least one processor may be configured to perform operations, including receiving a detection of a local pressure disturbance from at least one of the plurality of sensors, based on a location of the detected local pressure disturbance, determining a change in the user interface, and transmitting a command to the projector to modify a projected user interface according to the determined change.

Abstract: The present disclosure relates to interactive projection systems. In one implementation, a system for projecting a user interface may include a film affixed to a surface; a first projector configured to project a user interface onto the film; a second projector configured to project a plurality of light beams such that the plurality of light beams form a plane above the film that does not intersect the film; and an image sensor configured to receive reflections caused by the projected plurality of light beams. The system may further include at least one processor configured to receive an indication of a reflection from the image sensor; determine a location associated with the reflection; in response to the received indication, determine a change for the user interface based on the determined location; and transmit a command to the first projector to modify the projected user interface according to the determined change.

Abstract: The present disclosure relates to systems and methods for processing an image including a vehicle using machine learning. The system may include a processor in communication with a client device, and a storage medium storing instructions that, when executed, cause the processor to perform operations including: determining a location of the client device; receiving an image of a vehicle from the client device; matching, using a machine learning algorithm, the image to one or more images of vehicles in a database, the database listing vehicles located at the determined location and including images of the vehicles; retrieving vehicle information from the database based on the matched one or more images in the database; obtaining user information relating to a financing request for the vehicle; determining a real-time quote for the vehicle based on the vehicle information and user information; and transmitting the real-time quote for display on the client device.

Abstract: The present disclosure relates to systems and methods for processing an image including a vehicle using machine learning. The system may include a processor in communication with a client device, and a storage medium storing instructions that, when executed, cause the processor to perform operations including: receiving an image of a vehicle from the client device; extracting one or more features from the image; based on the extracted features and using a machine learning algorithm, determining a make and a model of the vehicle; obtaining user information relating to a financing request for the vehicle; determining a real-time quote for the vehicle based on the make, the model, and the user information; and transmitting the real-time quote for display on the client device.

Abstract: The present disclosure relates to image preprocessing to improve object recognition. In one implementation, a system for preprocessing an image for object recognition may include at least one memory storing instructions and at least one processor configured to execute the instructions to perform operations. The operations may include receiving the image, detecting a plurality of bounding boxes within the image, grouping the plurality of bounding boxes into a plurality of groups such that bounding boxes within a group have shared areas exceeding an area threshold, deriving a first subset of the plurality of bounding boxes by selecting bounding boxes having highest class confidence scores from at least one group, selecting a bounding box from the first subset having a highest score based on area and class confidence score, and outputting the selected bounding box.

Abstract: The present disclosure relates to systems, methods, and computer readable media for processing an image including a vehicle using machine learning. The systems can include determining a location of the client device. The systems can further include receiving a first image of a vehicle from an image sensor of the client device and matching, using machine learning, the first image to one or more images of vehicles in a vehicle database to identify the vehicle. The vehicle database can list vehicles located at the determined location of the client device and images of the vehicles. The systems can include retrieving vehicle information from the vehicle database, based on the identified vehicle, and obtaining comparison information based at least in part on the vehicle information. The systems can include estimating a quote for the vehicle based on the comparison information and transmitting the estimated quote for display on the client device.

Abstract: The present disclosure relates to interactive projection systems. In one implementation, an interactive projection system for a vehicle window may include a film having a plurality of sensors embedded therein, the plurality of sensors configured to detect local pressure disturbance on the film; a projector configured to project a user interface onto the film; and at least one processor. The at least one processor may be configured to perform operations, including receiving a detection of a local pressure disturbance from at least one of the plurality of sensors, based on a location of the detected local pressure disturbance, determining a change in the user interface, and transmitting a command to the projector to modify a projected user interface according to the determined change.

Abstract: An artificial intelligence system for identifying attributes in an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: extracting first features; categorizing the first images in a first group or a second group; modifying first metadata associated with each image in the first images to include a binary label; calculating a classification function; classifying a second plurality of images using the classification function; extracting second features from the second images classified in the first group; categorizing the second images in the first group by attribute; calculating an attribute identification function that identifies attributes of the second images; and identifying at least one attribute associated with a client image using the attribute identification function, the client image being received from the client device.

Abstract: Systems and methods for preventing machine learning models from negatively affecting mobile devices are provided. For example, a mobile device including a camera, memory devices, and one or more processors are provided. In some embodiments, the processors may be configured to provide images captured by the camera to a machine learning model at a first rate. The processors may also be configured to determine whether one or more of the images includes an object. If one or more of the images includes the object, the processors may be further configured to adjust the first rate of providing the images to the machine learning model to a second rate, and in some embodiments, determine whether to adjust the second rate of providing the images to the machine learning model to a third rate based on output received from the machine learning model.

Abstract: Systems and methods for preventing machine learning models from negatively affecting mobile devices are provided. For example, a mobile device including a camera, memory devices, and one or more processors are provided. In some embodiments, the processors may be configured to provide images captured by the camera to a machine learning model at a first rate. The processors may also be configured to determine whether one or more of the images includes an object. If one or more of the images includes the object, the processors may be further configured to adjust the first rate of providing the images to the machine learning model to a second rate, and in some embodiments, determine whether to adjust the second rate of providing the images to the machine learning model to a third rate based on output received from the machine learning model.

Abstract: A system for processing an image including a vehicle using machine learning can include a processor in communication with a client device, and a storage medium storing instructions that, when executed, cause the processor to perform operations including: receiving an image of a vehicle from the client device; extracting one or more features from the image; based on the extracted features and using a machine learning algorithm, determining a make and a model of the vehicle; obtaining user information relating to a financing request for the vehicle; determining a real-time quote for the vehicle based on the make, the model, and the user information; and transmitting the real-time quote for display on the client device.

Abstract: The present disclosure relates to interactive projection systems. In one implementation, a system for projecting a user interface may include a film affixed to a surface; a first projector configured to project a user interface onto the film; a second projector configured to project a plurality of light beams such that the plurality of light beams form a plane above the film that does not intersect the film; and an image sensor configured to receive reflections caused by the projected plurality of light beams. The system may further include at least one processor configured to receive an indication of a reflection from the image sensor; determine a location associated with the reflection; in response to the received indication, determine a change for the user interface based on the determined location; and transmit a command to the first projector to modify the projected user interface according to the determined change.

Abstract: A system for processing an image including a vehicle using machine learning can include a processor in communication with a client device, and a storage medium storing instructions that, when executed, cause the processor to perform operations including: determining a location of the client device; receiving an image of a vehicle from the client device; matching, using a machine learning algorithm, the image to one or more images of vehicles in a database, the database listing vehicles located at the determined location and including images of the vehicles; retrieving vehicle information from the database based on the matched one or more images in the database; obtaining user information relating to a financing request for the vehicle; determining a real-time quote for the vehicle based on the vehicle information and user information; and transmitting the real-time quote for display on the client device.

Abstract: The present disclosure relates to image preprocessing to improve object recognition. In one implementation, a system for preprocessing an image for object recognition may include at least one memory storing instructions and at least one processor configured to execute the instructions to perform operations. The operations may include receiving the image, detecting a plurality of bounding boxes within the image, grouping the plurality of bounding boxes into a plurality of groups such that bounding boxes within a group have shared areas exceeding an area threshold, deriving a first subset of the plurality of bounding boxes by selecting bounding boxes having highest class confidence scores from at least one group, selecting a bounding box from the first subset having a highest score based on area and class confidence score, and outputting the selected bounding box.

Abstract: A method for processing an image including a vehicle using machine learning can include determining a location of the client device. The method can further include receiving a first image of a vehicle from an image sensor of the client device and matching, using machine learning, the first image to one or more images of vehicles in a vehicle database to identify the vehicle. The vehicle database can list vehicles located at the determined location of the client device and images of the vehicles. The method can include retrieving vehicle information from the vehicle database, based on the identified vehicle, and obtaining comparison information based at least in part on the vehicle information. The method can include estimating a quote for the vehicle based on the comparison information and transmitting the estimated quote for display on the client device.

Abstract: The present disclosure relates to image preprocessing to improve object recognition. In one implementation, a system for preprocessing an image for object recognition may include at least one memory storing instructions and at least one processor configured to execute the instructions to perform operations. The operations may include receiving the image, detecting a plurality of bounding boxes within the image, grouping the plurality of bounding boxes into a plurality of groups such that bounding boxes within a group have shared areas exceeding an area threshold, deriving a first subset of the plurality of bounding boxes by selecting bounding boxes having highest class confidence scores from at least one group, selecting a bounding box from the first subset having a highest score based on area and class confidence score, and outputting the selected bounding box.

Abstract: An artificial intelligence system for identifying attributes in an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: extracting first features; categorizing the first images in a first group or a second group; modifying first metadata associated with each image in the first images to include a binary label; calculating a classification function; classifying a second plurality of images using the classification function; extracting second features from the second images classified in the first group; categorizing the second images in the first group by attribute; calculating an attribute identification function that identifies attributes of the second images; and identifying at least one attribute associated with a client image using the attribute identification function, the client image being received from the client device.

Abstract: The disclosed technology includes image-based systems and methods for object tracking within an asset area. Some exemplary methods include receiving, from an ingress detector, an ingress indication of a first object entering an asset area and receiving, from one or more image capture devices positioned to view respective portions of the asset area, data indicative of a plurality of captured images. The methods also include performing, by at least one processor, object classification of the first object based on the ingress indication, one or more of at least one of the plurality of captured images, and an identification signal detected by the ingress detector. The methods further include determining a first object location of the first object based at least in part on the object classification and outputting an indication of the first object location.

Abstract: The present disclosure relates to image preprocessing to improve object recognition. In one implementation, a system for preprocessing an image for object recognition may include at least one memory storing instructions and at least one processor configured to execute the instructions to perform operations. The operations may include receiving the image, detecting a plurality of bounding boxes within the image, grouping the plurality of bounding boxes into a plurality of groups such that bounding boxes within a group have shared areas exceeding an area threshold, deriving a first subset of the plurality of bounding boxes by selecting bounding boxes having highest class confidence scores from at least one group, selecting a bounding box from the first subset having a highest score based on area and class confidence score, and outputting the selected bounding box.