Neural network applications in resource constrained environments
Systems and methods are disclosed for applying neural networks in resource-constrained environments. A system may include a sensor located in a resource-constrained environment configured to generate sensor data of the resource-constrained environment. The system may also include a first computing device not located in the resource-constrained environment configured to produce a neural network structure based on the sensor data. The system may further include a second computing device located in the resource-constrained environment configured to provide the sensor data as input to the neural network structure. The second computing device may be further configured to determine a state of the resource-constrained environment based on the input of the sensor data to the neural network structure.
1. A system comprising:
a sensor located in a resource-constrained environment configured to generate first sensor data of the resource-constrained environment and second sensor data of the resource-constrained environment, wherein the first sensor data and the second sensor data are images of the resource-constrained environment;
a first computing device not located in the resource-constrained environment configured to produce a neural network structure based on the first sensor data;
a second computing device located in the resource-constrained environment configured to provide the second sensor data as input to the neural network structure, wherein the second computing device is further configured to determine a state of the resource-constrained environment based on the input of the second sensor data to the neural network structure; and
a controller located in the resource-constrained environment configured to control an indicator device based on the state of the resource-constrained environment determined by the second computing device,
wherein the second computing device is configured to determine the state of the resource-constrained environment at least in part by determining whether an abandoned item is present in the resource-constrained environment based on the input of the second sensor data to the neural network structure.
2. The system of claim 1 ,
wherein the resource-constrained environment comprises an interior of an automobile, and
wherein the first sensor data and the second sensor data are images of the interior of the automobile.
3. The system of claim 2 ,
wherein the controller is configured to control the indicator device based on the state of the resource-constrained environment determined by the second computing device and further based on an input from a technician.
4. The system of claim 2 ,
wherein the controller is configured to control the indicator device based on the state of the resource-constrained environment determined by the second computing device and further based on a signal received from an electronic transponder located external to the automobile.
5. The system of claim 2 ,
wherein the controller is further configured to perform an autonomous driving operation for the automobile based on the state of the resource-constrained environment determined by the second computing device.
6. The system of claim 2 ,
wherein the controller is configured to control the indicator device based on the state of the resource-constrained environment determined by the second computing device and further based on a presence of the automobile in a predetermined location for a rental car program.
7. The system of claim 1 ,
wherein indicator device is a mobile device, and
wherein the controller is configured to control the indicator device by transmitting information about an abandoned item being present in the resource constrained environment for display on a display component of the mobile device.
8. The system of claim 1 ,
wherein the second computing device is configured to determine whether an abandoned item is present in the resource-constrained environment at least in part by determining whether an article of trash is present in the resource-constrained environment.
9. A method comprising:
generating first sensor data of a resource-constrained environment and second sensor data of the resource-constrained environment using a sensor located in the resource-constrained environment, wherein the first sensor data and the second sensor data are images of the resource-constrained environment;
produce a neural network structure based on the first sensor data using a first computing device not located in the resource-constrained environment;
inputting the second sensor data to the neural network structure using a second computing device located in the resource-constrained environment;
determining a state of the resource-constrained environment using the second computing device based on the input of the second sensor data to the neural network structure; and
controlling an indicator device using a controller located in the resource-constrained environment based on the state of the resource-constrained environment determined by the second computing device,
wherein the determining the state of the resource-constrained environment comprises determining whether an abandoned item is present in the resource-constrained environment based on inputting the second sensor data to the neural network structure.
10. The method of claim 9 ,
wherein the resource-constrained environment comprises an interior of an automobile, and
wherein the first sensor data and the second sensor data are images of the interior of the automobile.
11. The method of claim 10 , wherein the controlling an indicator device is performed based on based on the state of the resource-constrained environment determined by the second computing device and further based on an input from a technician.
12. The method of claim 10 , wherein the controlling an indicator device is performed based on based on the state of the resource-constrained environment determined by the second computing device and further based on a signal received from an electronic transponder located external to the automobile.
13. The method of claim 10 , further comprising:
performing an autonomous driving operation for the automobile based on the state of the resource-constrained environment determined by the second computing device.
14. The method of claim 10 , wherein the controlling an indicator device is performed based on the state of the resource-constrained environment determined by the second computing device and further based on a presence of the automobile in a predetermined location for a rental car program.
15. The method of claim 9 , wherein indicator device is a mobile device, and wherein the controlling an indicator device comprises:
transmitting information about an abandoned item being present in the resource constrained environment for display on a display component of the mobile device.
16. The method of claim 9 , wherein the determining whether an abandoned item is present in the resource-constrained environment comprises:
determining whether an article of trash is present in the resource-constrained environment.
17. A system comprising:
a sensor located in a resource-constrained environment configured to generate first sensor data of the resource-constrained environment and second sensor data of the resource-constrained environment, wherein the first sensor data and the second sensor data are images of the resource-constrained environment;
a first computing device not located in the resource-constrained environment configured to produce a neural network structure based on the first sensor data;
a second computing device located in the resource-constrained environment configured to provide the second sensor data as input to the neural network structure, wherein the second computing device is further configured to determine a state of the resource-constrained environment based on the input of the second sensor data to the neural network structure; and
a controller located in the resource-constrained environment configured to control an indicator device based on the state of the resource-constrained environment determined by the second computing device,
wherein the second computing device is configured to determine the state of the resource-constrained environment at least in part by determining whether the state of the resource-constrained environment deviates from a normal state of the resource-constrained environment.
18. The system of claim 17 ,
wherein the resource-constrained environment comprises an interior of an automobile,
wherein the first sensor data and the second sensor data are images of the interior of the automobile, and
wherein the second computing device is configured to determine whether the state of the resource-constrained environment deviates from a normal state of the resource-constrained environment at least in part by determining the location of a car seat in the automobile.
19. The system of claim 17 ,
wherein the resource-constrained environment comprises an interior of an automobile,
wherein the first sensor data and the second sensor data are images of the interior of the automobile, and
wherein the second computing device is configured to determine whether the state of the resource-constrained environment deviates from a normal state of the resource-constrained environment at least in part by determining the location of a steering wheel in the automobile.
20. The system of claim 17 ,
wherein the resource-constrained environment comprises an interior of an automobile,
wherein the first sensor data and the second sensor data are images of the interior of the automobile, and
wherein the second computing device is configured to determine whether the state of the resource-constrained environment deviates from a normal state of the resource-constrained environment at least in part by determining whether an article of trash is present in the automobile.