VIDEO-BASED MOTION COUNTING AND ANALYSIS SYSTEMS AND METHODS FOR VIRTUAL FITNESS APPLICATION

1 . A computer-implemented method executable by a hardware processor for video processing, comprising:

receiving an input video of one or more persons from a camera;

detecting a sequence of human poses in the input video using an artificial intelligence (AI) based technique;

selecting a proper pose from among multiple poses in a given frame of the input video, to generate a sequence of proper poses;

detecting one or more key points in the sequence of proper poses;

computing changes in coordinates of the one or more key points;

computing a function of the changes in the coordinates of the one or more key points in the sequence of proper poses;

counting a given user movement as a repetitive motion of an activity based on the function; and

computing a plurality of statistics about the activity based on the counting.

2 . The computer-implemented method of claim 1 , wherein the activity is selected from the group consisting of running, jogging, walking, jumping, performing jumping jacks, squatting, and dribbling.

3 . The computer-implemented method of claim 1 , wherein the repetitive motion is selected from the group consisting of steps, jumps, squats, and dribbles.

4 . The computer-implemented method of claim 1 , wherein the selecting the proper pose comprises:

selecting a centered pose as the proper pose, when the hardware processor has detected multiple poses in a given frame.

5 . The computer-implemented method of claim 1 , wherein the selecting the proper pose comprises:

selecting the proper pose utilizing a human tracking algorithm.

6 . The computer-implemented method of claim 1 , wherein the one or more key points are selected from the group consisting of a body joints, a nose, an eyes, an ears, a chest, and a shoulders of a user.

7 . The computer-implemented method of claim 1 , wherein the function of the changes in the coordinates is selected from the group consisting of a mean, a median, and a single delta value selection.

8 . The computer-implemented method of claim 1 , wherein the function of the changes in the coordinates is a mean delta value, and wherein the method further comprises:

counting the given user movement as the repetitive motion when the mean delta value changes in a predetermined pattern.

9 . The computer-implemented method of claim 1 , further comprising:

applying a smoothing function on the coordinates of the one or more key points.

10 . The computer-implemented method of claim 1 , further comprising:

performing a checking process on the given user movement's metrics to invalidate the given user movement based on one or more criteria.

11 . The computer-implemented method of claim 10 , further comprising:

excluding the given user movement based on an associated rising period being more than a first threshold; and

excluding the given user movement based on an associated rising amplitude being smaller than a second threshold.

12 . The computer-implemented method of claim 10 , further comprising:

adjusting the checking process based on a parameter.

13 . The computer-implemented method of claim 10 , further comprising:

utilizing a gesture to control the checking process.

14 . The computer-implemented method of claim 1 , further comprising:

presenting one or more gamification elements based on at least one of the plurality of statistics.

15 . The computer-implemented method of claim 1 , wherein the input video is captured using the camera selected from the group consisting of a mobile device camera and a portable camera device.

16 . A non-transitory storage medium storing program code for video processing, the program code executable by a hardware processor, the program code when executed by the hardware processor causes the hardware processor to:

receive an input video of one or more persons from a camera;

detect a sequence of human poses in the input video using an artificial intelligence (AI) based technique;

select a proper pose from among multiple poses in a given frame of the input video, to generate a sequence of proper poses;

detect one or more key points in the sequence of proper poses;

compute changes in coordinates of the one or more key points;

compute a function of the changes in the coordinates of the one or more key points in the sequence of proper poses;

count a given user movement as a repetitive motion of an activity based on the function; and

compute a plurality of statistics about the activity based on the counting.

17 . The non-transitory storage medium of claim 16 , wherein the activity is selected from the group consisting of running, jogging, walking, jumping, performing jumping jacks, squatting, and dribbling.

18 . The non-transitory storage medium of claim 16 , wherein the program code to select the proper pose comprises program code to:

select a centered pose as the proper pose, when the hardware processor has detected multiple poses in a given frame.

19 . The non-transitory storage medium of claim 16 , wherein the program code to select the proper pose comprises program code to:

select the proper pose utilizing a human tracking algorithm.

20 . The non-transitory storage medium of claim 16 , wherein the program code to select the proper pose program code to:

select the proper pose utilizing a human tracking algorithm.