Computer Vision Use Cases in Sports Industry

A naked human eye is being gradually replaced with smart algorithms that do all the cumbersome analytics automatically. These capacities may help better analyze the crucial sports event moments to get more precise scores and judge more efficiently as a whole.

Although the use of CV in professional sports mostly requires pre-recorded content of high-definition, the technology is pretty in-depth efficient in processing video of any format, coming from any device. Where in particular this form of machine processing is applied? Let’s dive a bit deeper into the topic of using content processing via computer vision in the sports industry.

Common Moments Efficiently Analyzed Via CV

Besides the obvious use in the security systems most demanding for image quality (recognition of faces, dangerous objects, etc.), machine vision technologies are used in many other cases in sports:

• training process — the in-depth analysis of captured actions in swimming, gymnastics, athletics, skiing, and other sports where the technique of performing movements matters the most;
• refereeing — 3D simulations and video inspection of the offsides, outs, goals, photo finish in mass races; all;
• the rates of player activity during events — for instance, in tennis, the dynamics of players can be captured and analyzed based on their movements and even slight gestures;
• ball (puck, you name it) trajectories — these can be analyzed as well as predicted for further in-depth analytics;
• action camera stabilization and smart focus —artificial intelligence in sportsallows for real-time smoothing out of action frames and automated focus based on the density of activity and target actions in the field.

These are just some general capabilities modern sports events organizers get to employ. Let’s take a look at a bunch of particular cases in a bit greater detail.

Computer Vision Applications in Sports

Real-time action recognition in hockey

Specialists at the Shiraz University and the University of Waterloo did a whole paper on efficient recognition of actions in hockey. The main gist is that experts have come up with the so-called Action Recognition Hourglass Network (ARHN), which is a complex, multi-component visual data processing model.

In simple terms, the complex algorithm takes a piece of motion video content and converts it into a series of images. Another underlying algorithm within a Stacked Hourglass network then analyzes players’ positions (straight and crossover skating, pre- and post-shot poses) and classNameifies them.

These models have been used for the longest time to help issue the fairest, most precise scores in this and other types of sports out there.

Ball tracking systems in tennis

Precise tennis (as well as badminton and cricket) ball trajectories have been tracked in sports since the mid-2000s. Thus, specialized systems focus on multiple objects in the image that are similar in form to a ball. Upon detecting these, a 3-dimensional trajectory is built by connecting the ball movement pattern frame by frame.

This is where multiple camera angles and flexible motion capture are essential. The main purpose here, is the precise statement on whether the ball landed in or out of bounds during the game. On their deepest, most complex layers, the underlying algorithms can build predictions of ball trajectories based on various conditions (a player’s miss or such).

Based on such solutions, smart statistics are generated in real-time for 100% fair refereeing and reputable sport performance analytics.

Training activities analytics

Modern sport imposes higher demands not only on the athletes but also on the team of coaches. The key advantage in team sports is not so much the presence of “stars” as the proper organization of the team game, the assessment of the actions of each player, their interaction, and it is invaluable for the coach to develop effective tactics and game strategies.

Computer vision in sports analytics is a great tool for getting objective, up-to-date information in the conditions when just recording a video of a game field is not enough. Mathematical processing of the video stream allows getting the position of each player of opponents’ teams at each moment.

For many sports arenas and clubs, sports video analytics systems have now become a very profitable business. Even though the creation of such systems requires organizing the synchronous operation of dozens of cameras and powerful computing capabilities, the effort is usually well paid off in the long run.

Prevention of life-threatening situations

In NASCAR racing and similar kinds of sports where players experience potential life dangers, computer vision is used to timely detect and prevent vehicle malfunctions. This is where such systems, basically, save lives.

Commonly, huge Big Data-based databases of vehicle models are implemented to recognize particular cars, analyzing them in the tiniest detail during the event. Thus, experts get real-time reach into the car’s insides to track any small malfunctions which can lead to serious consequences.

For many sports arenas and clubs, sports video analytics systems have now become a very profitable business. Even though the creation of such systems requires organizing the synchronous operation of dozens of cameras and powerful computing capabilities, the effort is usually well paid off in the long run.

Fan mood and engagement analysis

A not so obvious application of a machine learning in sports analytics — organizers can recognize faces on the tribunes and identify emotions fans experience. This is made to stimulate the hype on the tribunes and build statistics on fan engagement as well as an event’s overall impact.

Smart sports journalism

Adding up to the previous point and expanding on the influence of a sports event on fans. Computer vision can also be beneficially used to generate impressive media content and more precisely report on the game highlights.

By analyzing the most standing-out, dynamic actions happening in the field (track, ring, etc.) based on some above-mentioned algorithms, an immediate focus on the most exciting happenings can be employed.

This is a crucial capability when it comes to live events to help keep all spectators on the edge of their seats. And apart from visual features, AI even helps to automatically commentate events without the help of live speakers (Automated Insights, for instance, developed a solution for real-time narratives based on Natural Language Recognition capacities).

The Specifics of Software For Computer Vision in Sports

The above applications and more make the world of sports a firmly-watched, ever so exciting, and fair competitive realm to organize. There are various types of solutions in the niche. Some of the leading examples include Sentio’s smart tracking and analytics systems; Stats Perform’s SportVU 2.0 with in-depth computer vision-based algorithms; GAMEFACE.AI with its in-depth analysis of strategic insights and other footage points; and more.

The available solutions are intricate systems to be integrated through hardware and software by a dedicated integrator specialist. The role of the integrator is limited to adapting the system based on the readymade standard components according to the requirements of a particular customer, its binding to a specific object, installation, and service entry.

Thus, the resolution and speed of the cameras are limited by the capabilities of the human operator, and the main focus is made on minimizing the volume of video recordings and the convenience of working with them.

Crucial points for getting the highest-quality analytics

The industry of computer vision system for tracking players in sports games has slightly different priorities arising from a much wider range of tasks, which cause a very limited distribution and use of "boxed" products. Due to the diversity of objects and tasks for observation, the requirements for image capture systems vary greatly.

First of all, it's supposed to be machine image processing, which entails requirements for the maximum transfer of details, diversity, and uniformity of shooting conditions to increase the efficiency (showing details), speed, and reliability (shooting conditions) of the algorithms. Based on our team’s experience, the main points in the selection of machine vision components are as follows:

• image quality, degree of detail, and speed (frame rate) must correspond to the mathematical algorithms used to solve various applied tasks;
• lighting conditions should be as stable and/or controlled as much as possible. In most cases it's artificial lighting;
• limited use or complete absence of automated functions such as auto exposure or autofocus in the camera. Everything is controlled by external software;
• the main information processing is performed on separate calculators since the complexity of the algorithms does not allow placing the required computing power in a compact camera body. In some cases, joint processing of images from multiple cameras is required. The type and power of the calculator are determined by the requirements of the specific task and the math used;
• high-speed interfaces for transmitting images with high resolution (details) and high frame rate (fixing fast processes) are required;
• software functionality from camera manufacturers is limited to a set of drivers for the flexible configuration of equipment. We develop application programs for each specific project.

Our Computer Vision Use Cases

Our team has experience of using computer vision in American football. The system was mainly used in recruiting. The customer requirements for functionality included:

• ability to track player speed,
• tracking and monitoring the effectiveness of players,
• visual information for the further analysis of game situations with the team.

As a result, when selecting athletes, a recruiter doesn’t have to attend competitions or travel to assess their abilities. They just have to watch a video with all the necessary analytics in the office or at home.

Conclusion

Artificial intelligence in sports makes refereeing, analyzing, highlighting, and satisfying fans easier to grasp and more efficient in the long run. When it comes to implementing an AI-based system for sports events, you have the ultimate choice of going for renowned yet costly solutions or ordering a cost-efficient custom local system.

Our team is an expert at developing and integrating sophisticated software of such kind. We will develop your CV-based software project taking into account the reasonable cost of equipment and user-friendly architecture. Feel free to contact us and get professional advice on common issues and questions.