Imaging device for basketball action characteristics

1. An imaging device comprising:

a housing configured to be coupled to a basketball backboard;

a lens disposed in the housing;

a control unit disposed in the housing, the control unit comprising one or more processors; and

a memory coupled to the one or more processors, wherein the one or more processors are configured to perform operations stored in the memory comprising:

performing line detection from captured image data;

providing, to a trained machine-learning model, adjusted image data based on one or more algorithms applied to the captured image data, wherein the one or more algorithms are based on whether or not lines are detected from the captured image data; and

generating, from output of the trained machine-learning model, data indicating characteristics of a basketball action associated with one or more users based on lines in the adjusted image data corresponding to a basketball court.

2. The imaging device of claim 1 , wherein performing line detection comprises determining whether a basketball court is identified in the captured image data, wherein determining whether the basketball court is identified comprises determining whether the captured image data comprises one or more of a free throw line, a three-point line, a half court line, a key line, a circle designating a center court, and out of bound lines.

3. The imaging device of claim 2 , wherein if it is determined that the captured image data comprises one or more of the free throw line, the three-point line, the half court line, the key line, the circle designating the center court, and the out of bound lines, producing, by the one or more algorithms, data identifying pixels illustrating the one or more of the free throw line, the three-point line, the half court line, the key line, the circle designating the center court, and the out of bound lines.

4. The imaging device of claim 3 , wherein the one or more processors are configured to calibrate the trained machine-learning model to process the captured image data.

5. The imaging device of claim 4 , wherein the one or more processors are configured to calibrate the trained machine-learning model to process the captured image data by labeling the pixels of the captured image data to correspond with the one or more of the free throw line, the three-point line, the half court line, the key line, the circle designating the center court, and the out of bound lines.

6. The imaging device of claim 5 , wherein the one or more processors are configured to provide a shot chart based on one or more of the free throw line, the three-point line, the half court line, the key line, the circle designating the center court, and the out of bound lines.

7. The imaging device of claim 6 , wherein the shot chart comprises an indication of one or more locations of one or more respective basketball shots of a user.

8. The imaging device of claim 1 , wherein if performing line detection from captured image data does not result in the detection of lines in the capture image data, then performing, by the one or more processors, operations stored in the memory comprising executing a pixel mapping algorithm using the captured image data.

9. The imaging device of claim 8 , wherein executing the pixel mapping algorithm comprises analyzing each frame of the captured image data for areas of interest.

10. The imaging device of claim 9 , wherein the areas of interest comprise one or more of color samples, a user's feet on a court, a user dribbling a basketball, a user shooting a basketball, a user running, and a user performing a basketball action.

11. The imaging device of claim 9 , wherein executing the pixel mapping algorithm comprises analyzing a set of pixels in the captured image data against one or more pixel templates.

12. The imaging device of claim 11 , wherein executing the pixel mapping algorithm comprises designating an event being detected in the captured image data when one or more pixel templates matches within a threshold amount to the set of pixels analyzed in the captured image data.

13. The imaging device of claim 8 , wherein executing the pixel mapping algorithm using the captured image data is applied to each frame of captured image data.

14. The imaging device of claim 1 , wherein the housing comprises a weatherproof enclosure for the lens, control unit, and memory.

15. The imaging device of claim 1 , wherein the lens comprises a fisheye lens.

16. The imaging device of claim 1 , wherein the housing comprises a dome-shape.

17. The imaging device of claim 1 , comprising a WiFi chip, a Bluetooth chip, and a printed circuit board (PCB) disposed in the housing.

18. The imaging device of claim 1 , wherein the imaging device comprises a RGB camera.

19. The imaging device of claim 1 , wherein the operations further comprise: generating, from a set of captured warped image data, data indicating characteristics of a basketball action associated with one or more users.

20. A method performed by an imaging device comprising:

performing line detection from captured image data;

providing, to a trained machine-learning model, adjusted image data based on one or more algorithms applied to the captured image data, wherein the one or more algorithms are based on whether or not lines are detected from the captured image data; and

generating, from output of the trained machine-learning model, data indicating characteristics of a basketball action associated with one or more users based on lines in the adjusted image data corresponding to a basketball court.