Player Pose Tracking

AI vision can recognize patterns between human body movement and pose over multiple frames in video footage or real-time video streams. For example, human pose estimation has been applied to real-world videos of swimmers where single stationary cameras film above and below the water surface. Those video recordings can be used to quantitatively assess the athletes’ performance without manually annotating the body parts in each video frame. Thus, Convolutional Neural Networks are used to automatically infer the required pose information and detect the swimming style of an athlete.

Markerless Motion Capture

Cameras can track the motion of the human skeleton without using traditional optical markers and specialized cameras. This is essential in sports capture, where players cannot be burdened with additional performance capture attire or devices.

Objective Athlete Performance Assessment

Automated detection and recognition of sport-specific movements overcome the limitations associated with manual performance analysis methods. Computer Vision data inputs can be used in combination with the data of body-worn sensors and wearables. Popular use cases are swimming analysis, golf swing analysis, over-ground running analytics, alpine skiing, and the detection and evaluation of cricket bowling.

Stroke Recognition

Computer vision applications are capable of detecting and classifying strokes (for example, classifying strokes in table tennis). Recognition or classification of movements involves further interpretations and labeled predictions of the identified instance (for example, differentiating tennis strokes as forehand or backhand). Stroke recognition aims to provide tools for teachers, coaches, and players to analyze table tennis games and to improve sports skills more efficiently.

Sports Team Behaviors Analysis

Analysts in professional team sports regularly perform analysis to gain strategic and tactical insights into player and team behavior (identify weaknesses, assess performance, and improve potentials). However, manual video analysis is typically time-consuming, where the analysts need to memorize and annotate scenes. Computer Vision techniques can be used to extract trajectory data from video material and apply movement analysis techniques to derive relevant team sport analytic measures for region, team formation, event, and player analysis (for example, in soccer team sports analysis).

Automated Media Coverage

AI vision technology can use video footage to interpret sports games and transmitting them to the media houses without necessarily going there with physical cameras. For instance, baseball has gained this advantage in the last few years with game news coverage automation.

Ball Tracking

Ball trajectory data are one of the most fundamental and useful information in evaluating players’ performance and analysis of game strategies. Hence, tracking of ball movement is an application of deep and machine learning to detect and then track the ball in video frames (Object Tracking). For example, Ball tracking is important in sports with large fields (e.g., Football) to help newscasters and analysts to interpret and analyze a sports game and tactics faster.

Goal-Line Technology

Camera-based systems can be used to determine if a goal has been scored or not to support the decision-making of referees. Unlike sensors, the vision-based method is noninvasive and does not require changes to the typical football devices.

Such Goal-Line Technology systems are based on high-speed cameras whose images are used to triangulate the ball’s position. A ball detection algorithm that analyzes candidate ball regions in order to recognize the ball pattern.

Event Detection in Sports

Deep Learning can be used to detect complex events from unstructured videos, like scoring a goal in a football game, near misses, or other exciting parts of a game that do not result in a score. This technology can be used for real-time event detection in sports broadcasts, applicable to a wide range of field sports.

Self-training Feedback

Computer Vision based self-training systems for sports exercise is a recently emerging research topic. While self-training is essential in sports exercise, a practitioner may progress to a limited extent without a coach’s instruction. For example, a yoga self-training application aims to instruct the practitioner to perform yoga poses correctly, assisting in rectifying poor postures and preventing injury. In addition, vision-based self-training systems can be used to give instructions on how to adjust the body posture.

Automatic Highlight Generation

Producing sports highlights is labor-intensive work that requires some degree of specialization, especially in sports with a complex set of rules that is played for a longer time (e.g., Cricket). An application example is automatic Cricket highlight generation using event-driven and excitement-based features to recognize and clip important events in a cricket match. Another application is the automatic curation of golf highlights using multimodel excitement features with Computer Vision.

Sports Activity Scoring

Deep Learning methods can be used for sports activity scoring to assess athletes’ action quality (Deep Features for Sports Activity Scoring). For example, automatic sports activity scoring can be used in diving, figure skating, or vaulting (ScoringNet is a 3D CNN network application for sports activity scoring). For example, a diving scoring application works by assessing the quality score of a diving performance of an athlete: It matters whether the athlete’s feet are together and their toes are pointed straight throughout the whole diving process.


Learn more about this game-changing technology and how it can impact your industry.

Learn More
Latest News

Read the latest exciting news from the VisionAppster team.

Company Overview

We're driven to help you succeed!

Get to know Us


Schedule a Demo

See VisionAppster in action!

Schedule it

Contact Us

For questions or more information

Contact Us
Contact Us