Nex Team Inc.

Man Hon Chan

Keng Fai Lee

On Loy Sung

Qi Zhang

Wing Hung Chan

Tin Yin Lam

Jorge S Fino

Daniel Dejos

Shing Fat Mak

A computer-implemented method for video processing is disclosed. The method includes 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. In some embodiments, the activity is running, jogging, walking jumping, performing jumping jacks, squatting, and/or dribbling.

Video-based motion counting and analysis systems and methods for virtual fitness application

A system and method for implementing a virtual fitness application are disclosed. Embodiments may be built for various platforms, including web browsers and mobile phones. One embodiment uses pose estimation to count the repetitive motions of a fitness activity (e.g., running). In one embodiment, to count the repetitive motions, a repetitive motion counting process is used that is based on computing differences of Y-coordinates of key points of the user. The repetitive motion counting process includes selecting a proper pose of the user; computing one or more delta values (corresponding to Y-coordinate changes of the key points); and counting a given user movement as a repetitive motion based on a function (e.g., average) of the delta values. One embodiment uses the pose estimation and repetitive motion count results to provide a gamified experience, for example, a leaderboard, a stats report, instant visual feedback, badges, coins, and a social experience.

Methods and systems for determining a ball shot attempt location on a ball court are disclosed. The method includes receiving an input video, where the input video includes the ball shot attempt and one or more players; receiving a declared ball shot attempt; determining a potential shooter by detecting a moving object flow in the input video; determining a potential shooter image region in the image plane by analyzing an overlapping image area of the ball trajectory and the potential shooter; determining a shooter foot-on-court time; determining a most probable pair of shooter legs in the potential shooter image region; determining a shooter foot location based on the most probable pair of shooter legs; and determining a ball shot attempt location on the ball court by applying a camera projection to the shooter foot location.

Methods and systems for determining ball shot attempt location on ball court

Methods and systems for determining and classifying a number of repetitive motions in a video are described, and include the steps of first determining a plurality of images from a video, where the images are segmented from at least one video frame of the video. Next, performing a pose detection process on a feature of the images to generate one or more landmarks. Next, determining one or more principle component axes on points associated with a given landmark. Finally, determining at least one repetitive motion based on a pattern associated with a projection of the points onto the one or more principle components. In some embodiments, the disclosed methods can classify the repetitive motions to respective types. The present invention can be implemented for convenient use on a mobile computing device, such as a smartphone, for tracking exercises and similar repetitive motions.

Repetition counting and classification of movements systems and methods

Methods and systems for determining a ball shot attempt result, the ball shot attempt captured by a camera on a mobile computing device, are disclosed. The methods and systems perform steps comprising receiving an input video captured using the camera on the mobile computing device, wherein the input video comprises a ball shot attempt at a goal; receiving a declared ball shot attempt, the declared ball shot attempt detected by analyzing a ball trajectory in the input video; detecting an element movement associated with an element of the goal; and determining the ball shot attempt result based on the element movement and the ball trajectory. In some embodiments, the element of the goal is a net, and the declared ball shot attempt is detected by analyzing the ball trajectory in a ball shot attempt Region of Interest (ROI) within frames of the input video.

Methods and systems for ball game analytics with a mobile device

Methods and systems for detecting a player in a gaming environment using a mobile device are disclosed. The methods and systems perform the steps of receiving calibration data from a camera of a user device, the calibration data comprising a first reference point associated with the gaming environment, a second reference point associated with the gaming environment, and sensor data associated with the user device; computing a camera projection based on the first reference point, the second reference point, and the sensor data obtained from the user device; receiving an input video of the gaming environment, wherein the input video is captured using the camera, and wherein frames of the input video comprise at least one player in the gaming environment; identifying an image location of the player by performing a computer vision algorithm on the input video; and determining a real-world location of the player based on the image location and the camera projection.

Methods and systems for player location determination in gameplay with a mobile device

Methods and systems for ball shot attempt detection and game analytics generation are disclosed. The methods and systems perform steps to receive an input video of a ball gameplay, wherein the input video was captured using a stationary camera, and wherein frames of the input video comprises a goal; identify a Region of Interest (ROI) surrounding the goal by performing a first computer vision algorithm on the input video; detect a ball within the ROI during a shot attempt and determining a trajectory of the ball by performing a second computer vision algorithm on the input video; and identify a player relevant to the shot attempt based on the trajectory of the ball. In some embodiments, the computer vision algorithms comprise a convolution neural network (CNN). The present invention uses computer vision techniques to enable a resource-limited mobile device such as a smartphone to efficiently execute the new process.

Growing community of inventors

Hong Kong, China

Man Hon Chan