Methods and systems for determining a displacement of a peripheral device are provided. In one example, a peripheral device comprises: an image sensor, and a hardware processor configured to: control the image sensor to capture a first image of a surface when the peripheral device is at a first location on the surface, the first image comprising a feature of the first location of the surface; execute a trained machine learning model using data derived from the first image to estimate a displacement of the feature between the first image and a reference image captured at a second location of the surface; and determine a displacement of the peripheral device based on the estimated displacement of the feature.

A method, system, and computer-readable medium, for detecting whether an eye blink or non-blink is captured in the image. The method includes filtering, from the image, one or more objects that are predicted to be unsuitable for determining whether an eye blink or no-blink is captured in the image, to provide a filtered image. The method also includes correlating the filtered image with a reference image, and determining, based on the correlating, whether the eye blink or non-blink is captured in the image. The eye blink is a full eye blink or a partial eye blink, and the images may be sequentially captured IR SLO images, in one example embodiment herein. Images determined to include an eye blink can be omitted from inclusion in a final (e.g., OCT) image.

In one embodiment, a computing system is configured to, during a first tracking session, detect first landmarks in a first image of the environment surrounding a user, and determine a first location of the user by comparing detected first landmarks to a landmark database. During a second tracking session, the computing system captures motion data and estimates a second location of the user based on the motion data and first user location. Based on the motion data and first user location, the computing system detects landmarks in a second image at a second location. The system accesses expected landmarks from the landmark database visible at the second location and determines the estimated second location of the user is inaccurate by comparing the expected landmarks with the second landmarks. The computing system re-localizes the user by comparing the landmarks in the landmark database and third landmarks in a third image.

Various methods and systems are provided for tracking a biopsy target across one or more images. In one example, a method includes determining a position of a biopsy target in a selected image of a patient based on an image registration process with a reference image of the patient, and displaying a graphical representation of the position of the biopsy target on the selected image.

Provided is a method and apparatus for eye tracking. An eye tracking method includes detecting an eye area corresponding to an eye of a user in a first frame of an image; determining an attribute of the eye area; selecting an eye tracker from a plurality of different eye trackers, the eye tracker corresponding to the determined attribute of the eye area; and tracking the eye of the user in a second frame of the image based on the selected eye tracker, the second frame being subsequent to the first frame.

This document discloses system, method, and computer program product embodiments for mitigating the addition of false object information to a track that provides a spatial description of an object, such as a track of radar data or lidar data. The system will analyze two or more frames captured in a relatively small time period and determine whether one or more parameters of an object detected in the frames remain consistent in a specified model. Models that the system may consider include a constant velocity model, a surface model, a constant speed rate model or a constant course rate model. If one or more parameters of the detected object are not consistent over the sequential frames in the specified model, the system may prune the track to exclude one or more of the sequential frames from the track.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing a video. The method includes acquiring a video, where the video includes at least a current frame and a previous frame that are adjacent to each other. The method further includes determining, based on a first pixel value of a pixel in the current frame and a second pixel value of a corresponding pixel in the previous frame, whether the current frame has changed relative to the previous frame. The method further includes determining availability of the current frame for a computer vision task if it is determined that the current frame has changed relative to the previous frame. With the method, video data that needs to be processed is reduced, the task load of a computing device is lowered, system power consumption is improved, and data processing efficiency is improved.

The present invention relates to a CPR posture monitoring model and system, the model being configured to: based on human skeleton point data extracted from CPR moves, at least compute arm-posture angle data and GC matching angle data related to the CPR moves, and determine whether the CPR moves are qualified by comparing arm-posture angle data and GC matching angle data to a CCP qualification threshold; wherein human skeleton point data are extracted from first move data collected by a first optical component and second move data collected by a second optical component simultaneously, wherein an included angle between collection directions of the two optical components ranges between 30 and 90 degrees. By constructing and applying a CPR posture monitoring model defined by data like GC matching angle, evaluation result has higher accuracy. Compared with the prior art where the equipment positions must be exactly the same as the equipment positions when the data are acquired for the AI algorithm to compute the CPR mistakes, the disclosed method does not require that the equipment positions must be exactly the same as the equipment positions when collecting the data and could tolerate inaccuracy or error in the equipment positions, besides, the disclosed method could realize harmless quality control of CPR moves.

A displacement meter that measures displacement of an object includes a calculation circuit which calculates a displacement amount of the object using a cross-correlation function of plural images detected at different timings by a photoelectric conversion element array. The calculation circuit performs a Fourier transform on the images, applies a band-pass filter to the images having undergone the Fourier transform, and calculates the cross-correlation function using the images to which the band-pass filter has been applied. Assuming that a magnification of a light-receiving optical assembly is M, the number of pixels in the photoelectric conversion element array is N, and a pixel pitch is P (um), a low cut-off frequency HPF of the band-pass filter and a high cut-off frequency LPF of the band-pass filter satisfy: 3M/(N×P)≤HPF≤10M/(N×P), 40M/(N×P)≤LPF≤60M/(N×P).

Methods, systems, and computer-program products used for assessing athletic ability and generating performance data. In one embodiment, athlete performance data is generated through computer-vision analysis of video of an athletic performing, e.g., during practice or gameplay. The generated performance data for the athlete may include, for example, maximum speed, maximum acceleration, time to maximum speed, transition time (e.g., time to change direction), closing speed (e.g., time to close the distance to another athlete), average separation (e.g., between the athlete and another athlete), play-making ability, athleticism (e.g., a weighted computation and/or combination of multiple metrics), and/or other performance data. This performance data may be used to generate and/or update a profile associated with the athlete, which can be utilized for recruiting, scouting, comparing, and/or assessing athletes with greater efficiency and precision.