Nex Team Inc.

Wing Hung Chan

Keng Fai Lee

Qi Zhang

On Loy Sung

Long Mak

Wang Fai Ng

Tin Yin Lam

Ian C Smith

Man Hon Chan

Arron Mollet

Bradford Lewis Farkas

David Richard Schmierer

Kin Ming Law

Alexander Wu

Methods and systems for touchless control of a device include receiving at least one image; determining a first map, the first map including a function that generates a correspondence between data associated with the image to a feature space; determining a second map including a representation of a user in the image based on the first map; determining a third map comprising a field associated with the data in the image; determining a characteristic of the user based on one or more of the second map and the third map; and tracking the characteristic of the user to generate a historical data of the characteristic for performing the touchless control of the device.

Methods and systems for touchless control with a mobile device

Methods and systems for remote multiplayer gameplay are disclosed. An example method can include joining a multiplayer interactive physical game including first and second players competing in an activity; capturing a first game video of a first player in a first play of the multiplayer interactive physical game, using a first camera on a first mobile computing device in a first gaming area; determining a first object flow associated with the first player from the first game video, by performing an algorithm on one or more frames of the first game video to detect one or more key points of the first player; generating a first analytic based on the first object flow; receiving a second analytic associated with the second player in a second gaming area; and generating a feedback to the first player based on the first analytic or the second analytic.

Remote multiplayer interactive physical gaming with mobile computing devices

A computer implemented method for facilitating training of body-eye coordination using a computing device having access to a camera is disclosed. The method includes receiving a training video of a player from the camera; superimposing a visual cue onto the training video; extracting a body posture flow of the player from the training video by performing a computer vision algorithm on one or more frames of the training video; determining whether the player has responded to the visual cue by analyzing the body posture flow of the player; and generating a feedback to the player in response to determining that the player has responded to the visual cue. Multi-player embodiments of the present invention are also disclosed.

Methods and systems for facilitating interactive training of body-eye coordination and reaction time

Methods and systems for performing location and movement determination in a three-dimensional environment using a device with a camera are disclosed. The methods and systems perform the steps of capturing an image of a user using the camera of the computing device, identifying the user from the image using a first algorithm, determining a first physical information associated with the user using a first algorithm, determining a second physical information of the user based on the first physical information and an input parameter of the user using a search process, and determining the location of the user in the environment based on the first physical information and the second physical information.

User analytics using a camera device and associated systems and methods

Methods and systems for touchless control of a device include receiving an image from a camera and applying a machine learning technique the image to determine a first map, the first map including a function that generates a correspondence between data associated with the image to a feature space. A second map including a representation of a user in the image can be determined based on the first map. Further, a pose of the user can be determined based on the second map. The pose of the user and a portion of a body of the user can be tracked to determine a mapping between the portion of the user's body and an input feature of a device. The input feature can be modified based on the mapping to enable a touchless interaction between the user and the device.

Methods and systems for image clustering are described, and include first receiving a plurality of images from a video of a game. Next, determining a first feature vector of a first player from the plurality of images by inputting the plurality of images to an artificial intelligence (AI)-based process. Next, dividing the plurality of images into a first subset of images and a second subset of images based on the first feature vector. Next, receiving a first identifier tagging the first player in a first image of the first subset. Finally, identifying a second player in a second image in the second subset by propagating the first identifier of the first subset, based on a distance measure associated with the first feature vector from the artificial intelligence (AI)-based process. Running on a mobile computing device, the method allows multiplayer tagging to be easily performed in almost any environment.

Methods and systems for multiplayer tagging using artificial intelligence

Methods and systems for multi-player training of body-eye coordination using mobile computing devices each having a camera are provided. The methods and systems execute instructions to capture a first training video of a first of player, using a first camera on a first mobile computing device; superimpose a visual cue onto the first training video at a visual cue location and for a cue period starting from a first time instant; determine whether the first player has responded to the visual cue at a second time instant within the cue period; receive a notification from a second mobile computing device, where the notification was generated in response to determining that a second player has responded to the visual cue at a third time instant within the cue period; and in response to a determination that the first player has responded to the visual cue and to the notification, generate a feedback to the first player.

Interactive training of body-eye coordination and reaction times using multiple mobile device cameras

Methods and systems for real-time generation of ball shot analytics are disclosed. The methods and systems perform steps for ball and posture detection, ball and posture flow generation, shot event identification and classification, and shot analytics generation based on identified shot events and shooter posture flow. Embodiments of the present invention use computer vision techniques to enable a resource-limited mobile device such as a smartphone to conduct the aforementioned steps. Also disclosed are benefits of the new methods, and alternative embodiments of implementation.

Methods and systems for generating sports analytics with a mobile device

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 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 image clustering are described, and include determining a plurality of images from a video of a game, the video captured by a camera on a mobile device, where at least one image of the plurality of images is segmented from a video frame of the video; determining a feature vector from the at least one image; dividing the images into a first subset and a second subset based on the feature vector; tagging a first player in a first image of the first subset with an identifier, where the identifier differentiates the images in the first subset to a plurality of players; and identifying a second player in a second image in the second subset by propagating the identifier of the first subset, based on a distance measure associated with the feature vector. Running on a mobile computing device, this invention allows multiplayer tagging to be easily performed in almost any environment.

Methods and systems for multiplayer tagging for ball game analytics generation with a mobile computing device

Methods and systems for performing location and movement determination in a three-dimensional environment using a mobile device with a camera are disclosed. The methods and systems perform the steps of capturing one or more images of a user using the camera on the mobile computing device, determining the user from the images using a machine learning algorithm, determining a pose associated with the user using a machine vision algorithm, determining a depth of the user with respect to the camera based on the pose and an input height of the user using a binary search process, and determining the location of the user in the environment based on the pose and the depth.

User analytics using a mobile device camera and associated systems and methods

Methods and systems for virtual coaching and performance training using a mobile device are disclosed. The methods and systems perform the steps of capturing a training video of one or more players, using the camera on the mobile computing device; superimposing a visual cue onto the training video at a first location and for a cue period starting from a first time instant; determining whether at least one of the one or more players has responded to the visual cue at a second time instant within the cue period, by analyzing a body posture flow of each player between the first time instant and the second time instant, wherein each body posture flow is extracted from the training video by performing a computer vision algorithm on one or more frames of the training video; and in response to determining that at least one player has responded to the visual cue, generating a feedback to the one or more players.

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.

Growing community of inventors

Hong Kong, China

Wing Hung Chan