Interactive basketball system
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a basketball backboard. The basketball backboard includes a display screen, a plurality of sensors configured to generate sensor data regarding a shot attempt of a user, imaging devices configured to generate image data of the shot attempt, a speaker, and a control unit. The control unit can receive (i) the sensor data from the plurality of sensors and (ii) the image data from the imaging devices. Based on the received sensor data, the control unit can determine whether the shot attempt was successful. Based on the received image data and whether the shot attempt was successful, the control unit can generate analytics that indicate characteristics of the user and the shot attempt and recommendations for improving the shot attempt for subsequent shot attempts. The control unit can provide output data representing the analytics.
1. A system comprising:
a housing configured to be coupled to a basketball backboard comprising:
an imaging device configured to generate image data of an area of a basketball court in a field of view of the imaging device;
a control unit configured to:
receive the generated image data from the imaging device;
generate, in the received image data, a virtual geometric region comprising a rim connected to the basketball backboard;
generate data (i) identifying one or more users on the basketball court, (ii) identifying a basketball associated with one of the one or more users, (iii) identifying an action performed by the one of the one or more users associated with the identified basketball, and (iv) identifying characteristics of the action performed by the one of the one or more users;
provide one or more of the generated data for display on a display screen; and
a mounting attachment configured to couple the housing to the basketball backboard.
2. The system of claim 1 , wherein the control unit is configured to provide the received image data to a machine learning model configured to:
generate the data (i) identifying one or more users on the basketball court, (ii) identifying a basketball associated with one of the one or more users, (iii) identifying an action performed by the one of the one or more users associated with the identified basketball, and (iv) identifying characteristics of the action performed by the one of the one or more users; and
provide one or more of the generated data from the machine learning model for display on the display screen.
3. The system of claim 2 , wherein the machine learning model is further configured to track the basketball associated with the one of the one or more users and track a location of the one of the one or more users across one or more frames of the generated image data.
4. The system of claim 3 , wherein the machine learning model is further configured to generate locational coordinates of the one of the one or more users and locational coordinates of the identified basketball across the one or more frames of the received image data.
5. The system of claim 2 , wherein the machine learning model is a convolutional neural network.
6. The system of claim 2 , wherein the control unit is configured to:
train the machine learning model to generate the data that (i) identifies the one or more users on the basketball court, (ii) identifies the basketball associated with the one of the one or more users, (iii) identifies the action performed by the one of the one or more users associated with the identified basketball, and (iv) generates characteristics of the action performed by the one of the one or more users,
wherein the machine learning model is trained using images and/or video of users performing one or more basketball actions, the basketball actions comprising (i) dribbling a basketball, (ii) shooting the basketball from various location in relation to multiple housing, (iii) moving while dribbling the basketball, and (iv) adjusting body posture of the users with the basketball.
7. The system of claim 6 , wherein the generated data that identifies the one of the one or more users on the basketball court comprises a statistical likelihood that indicates a confidence in the identification for the one of the one or more users.
8. The system of claim 6 , wherein the generated data that identifies the basketball associated with the one of the one or more users comprises a statistical likelihood that indicates a confidence in the identification of the basketball associated with the one of the one or more users.
9. The system of claim 2 , wherein the control unit is configured to provide for display a notification to indicate the machine learning model is learning to identify the one or more users.
10. The system of claim 1 , wherein the action performed by the one of the one or more users comprises (i) performing a basketball shot, (ii) dribbling the basketball, and (iii) passing the basketball.
11. The system of claim 1 , wherein the control unit is configured to:
determine whether the identified basketball enters the virtual geometric region in response to the identified action performed by the one of the one or more users;
in response to determining that the identified basketball enters the virtual geometric region, determine if the identified basketball passes through the rim of the housing; and
in response to determining that the identified basketball passes through the rim of the housing, provide data indicative of a shot counter for display to indicate whether the identified basketball passes through the rim.
12. The system of claim 11 , wherein the control unit is configured to:
in response to determining the identified basketball passes through the rim of the housing, associate a make of the identified action from one of the one or more users performing the identified action.
13. The system of claim 11 , wherein the control unit is configured to:
in response to determining the identified basketball does not pass through the rim of the housing, associate a miss of the identified action from one of the one or more users performing the identified action.
14. The system of claim 1 , further comprising:
a network interface coupled to the control unit, the network interface configured to connect the system to one or more additional systems, transmit image data captured from the system to the one or more additional systems, receive image data captured from the one or more additional systems, and provide the received image data to the control unit for display.
15. The system of claim 1 , wherein the imaging device is further configured to draw a box around the one or more users and the basketball in the one or more frames of the received image data.
16. The system of claim 1 , wherein the virtual geometric region comprises a portion coinciding with the rim.
17. A computer implemented method comprising:
generating, by an imaging device of a housing coupled to a basketball backboard by a mounting attachment, image data of an area of a basketball court in a field of view of an imaging device;
receiving, by a control unit of the housing, the generated image data from the imaging device;
generating, in the received image data, a virtual geometric region comprising a rim connected to the basketball backboard;
generating data, by the control unit (i) identifying one or more users on the basketball court, (ii) identifying a basketball associated with one of the one or more users, (iii) identifying an action performed by the one of the one or more users associated with the identified basketball, and (iv) identifying characteristics of the action performed by the one of the one or more users; and
providing, by the control unit, one or more of the generated data for display on a display screen.
18. The computer implemented method of claim 17 , wherein in the generating data step, the control unit provides the received image data to a machine learning model to generate the data (i) identifying one or more users on the basketball court, (ii) identifying a basketball associated with one of the one or more users, (iii) identifying an action performed by the one of the one or more users associated with the identified basketball, and (iv) identifying characteristics of the action performed by the one of the one or more users; and wherein in the providing step, the control unit provides one or more of the generated data from the machine learning model for display on the display screen.
19. The computer implemented method of claim 18 , further comprising:
tracking, by the machine learning model, the basketball associated with the one of the one or more users across one or more frames of the generated image data; and
tracking, by the machine learning model, a location of the one of the one or more users across the one or more frames of the generated image data.
20. The computer implemented method of claim 18 , further comprising:
generating, by the machine learning model, locational coordinates of the one of the one or more users across the one or more frames of the generated image data; and
generating, by the machine learning model, locational coordinates of the identified basketball across the one or more frames of the received image data.
21. The computer implemented method of claim 17 , wherein the action performed by the one of the one or more users comprises (i) performing a basketball shot, (ii) dribbling the basketball, and (iii) passing the basketball.
22. The computer implemented method of claim 17 , further comprising:
determining, by the control unit, whether the identified basketball enters the virtual geometric region in response to the identified action performed by the one of the one or more users;
in response to determining that the identified basketball enters the virtual geometric region, determining, by the control unit, if the identified basketball passes through the rim of the housing; and
in response to determining that the identified basketball passes through the rim of the housing, providing, by the control unit, data indicative of a shot counter for display to indicate whether the identified basketball passes through the rim.
23. The computer implemented method of claim 17 , wherein the virtual geometric region comprises a portion coinciding with the rim.