Embedded Vision in Sports Analytics – Relevance and Applications
Embedded vision is growing at a rapid pace. The COVID-19 pandemic accelerated this growth with unmanned monitoring and touchless transactions powering various sectors. Sports analytics is one such sector that has seen wide adoption when it comes to the use of embedded camera modules. And this is not just limited to 1 or 2 sports & games. As sports authorities and organizations realize the benefits of using embedded cameras for sports analytics more and more, newer games are finding ways to use camera modules to their advantage.
But what is sports analytics? Why do games need embedded cameras – especially given that professional broadcasting equipment is used? What are the different games in which embedded vision is (or can be) used? Let’s explore all these in today’s article on how embedded vision is revolutionizing the sports industry.
What is Sports Analytics?
Sports analytics broadly means using data available from sports and games to perform tasks such as:
- Analyzing player and team performance.
- Consolidating various statistics and slicing and dicing historical data.
- Analyzing playing patterns and tactics (of teams as well as individual players).
- For betting purposes by observing matches and deriving insights based on the videos.
Sports analytics is usually divided into on-field analytics and off-field analytics. As the terms suggest, the former is about analyzing the game or event on the field to help players and coaches do the right analyses, while the latter is about using the data to help sports authorities with their decision-making.
Sports analytics is a hot job in the market with sports analysts employed in professional sports clubs, college sports groups, sports analytics companies, and wearable technology manufacturers. They break down the data collected from games to help coaches, players, and team managers enhance team performance, come up with game action plans, improve player fitness levels, etc.
How is Embedded Vision Used in Sports Analytics?
Compared to other industries where embedded vision is used, sports analytics is a relatively new entrant. This is because it took some time for a few startups to emerge in the space for widely turning to embedded vision as a technology to be used for sports analytics.
While for a long time, professional sports relied on heavy broadcasting equipment to collect data for analysis, amateur games had no way to capture and broadcast matches. Embedded vision made a breakthrough here by offering tier 3 or tier 4 sports clubs the ability to broadcast their matches automatically by installing embedded vision cameras on the field. This also meant that, now these amateur sports clubs can analyze player and ball tracking data just like how it is done in a professional match.
Embedded vision is also used in professional sports matches to aid analytics. While professional cameras capture the match for broadcasting, ultra wide-angle embedded cameras can be installed in the match arena to analyze player and ball movements. This data can be used during in-match or post-match analyses to formulate game plans and strategies.
In general, embedded vision systems in sports analytics serve two purposes:
- Capture the match for online broadcasting (this is applicable only for matches occurring in colleges, schools, and tier 3 clubs where professional broadcasting is not available or is unaffordable).
- Track player movements, ball trajectories, and other sports equipment (like a baseball bat or golf club). The hawk-eye technology widely used in all types of sports and games is an example of the application of embedded camera-enabled ball tracking.
The former is very straightforward. Only that you need to know the right camera type to use by taking into consideration factors like resolution, field of view, lens distortion, image stitching, etc.
The latter involves the use of artificial intelligence and machine learning algorithms that can identify players and the ball in a captured scene, that too in real-time. This is usually done to zoom in on a player, ball, or other sports equipment to closely analyze their movements. To do this, high-end AI processors like NVIDIA Jetson AGX Xavier or NVIDIA Orin are required (in most cases) since these systems are becoming more and more data-hungry.
Difference Between Embedded Camera-based Sports Analytics and Professional Broadcasting
In professional matches (say major football leagues across the world), cameras are primarily used for broadcasting purposes. This would require a crew to manage the setup and operations during the match. That’s how we get to view the matches on TV and online streaming platforms. However, as mentioned before, the practice of using embedded cameras alongside broadcasting cameras is also seen in the professional sports world.
When it comes to the use of embedded vision in sports broadcasting and analytics, cameras are deployed to focus on specific areas of the venue – such as players and the ball. Also, embedded cameras help in officiating amateur matches where high-end professional cameras cannot be afforded.
In addition, the video data obtained from embedded cameras can be analyzed with the help of AI and ML algorithms to help with forming game plans and strategies. This was earlier possible only in professional matches. The use of embedded vision for this purpose is becoming a game-changer for coaches and team managers.
Advantages of Using Embedded Vision in Sports Analytics
Embedded cameras offer multiple advantages to sports authorities and teams. Amateur sports clubs, colleges, and schools that cannot afford to use expensive equipment to broadcast live matches will find a cost-effective solution in embedded vision cameras.
In addition to this, the following are the advantages of using embedded vision in sports analytics:
- Embedded vision systems are convenient to maintain and operate since they do not need any supervision.
- They can do image stitching on-device. This helps to cover the entire field with a limited number of camera installations by creating a panoramic view (this might also require the use of ultra-wide lenses with a large field of view in addition to having large sensors).
- Embedded cameras usually come with a minimum MTBF (Mean Time Between Failure) of 5 years. This means that you could install them and not worry about it for 5 years or more.
- In professional matches, while the expensive camera equipment can be used for broadcasting purposes, specialized embedded cameras can be installed for sports analytics. This offers the ability to optimize both types of cameras for their respective functions.
With these key advantages, the application of embedded vision in sports analytics has grabbed attention in regions such as the US, UK, Europe, and even Asia Pacific.
Sports and Games Where Embedded Vision is Used for Sports Analytics
Interesting use cases are emerging for the application of embedded vision in sports analytics every day. While many of them are in the experimentation phase, there are many games where the technology has reached a certain level of maturity. We will look at both examples in this section.
In tennis, cameras are used as digital assistants or coaches for individual players. Cameras also help coaches improve their players’ performance through pose estimation and racket swing analysis.
Computer vision models like Convolutional Neural Networks are used for this where the position of limbs and joints are analyzed from the videos and images captured.
Advanced computer vision and ML (Machine Learning) algorithms can also help compare a player’s characteristics with those of professional players to gauge where he/she stands with respect to global standards.
Soccer is a highly competitive sport where a tiny bit of enhancement can make a significant difference in match outcomes. It is a sport that has been using sports analytics for many years now. Embedded vision found its way into soccer probably in the last 3 to 5 years.
In soccer, embedded cameras are deployed on the ground to capture matches as well as track player and ball movements. Cameras can help measure player running speed, distance covered, number of shots taken, ball movements, and more. You can also use embedded cameras to do player tagging – helping you to focus on specific players of interest during a match.
All relevant analyses can be done at a player as well as team level. The beauty of using embedded vision cameras as opposed to a professional camera crew is that you can completely automate the process of image and video capture. No supervision is involved. No manual adjustments. No additional personnel is required. It’s all automated once you install the cameras on the field.
This is a very peculiar use case of embedded vision. Horse betting is a popular entertainment in many countries with money spent in millions of dollars. It works based on the idea that the horse with the highest odds of winning gets the lowest payout if it wins. The odds are of course calculated based on the past winning records of the horse.
But for the wagers to be wiser about their betting, they need to pick a horse that has a high likelihood of winning on the day despite the odds being low (that’s when the wagers get the best payout). Using embedded cameras to analyze horses’ running patterns and performances makes this possible.
Unlike tennis and soccer, as of today, the application of embedded vision in horse betting is evolving. We need to wait and see how popular this is going to get in the coming years.
Forming a game plan based on the opposition’s way of playing can be game-changing in Basketball. Embedded cameras can be installed in basketball arenas to help team coaches and players analyze the opposition’s tactics as well as their own team’s performance. Cameras can also be used to delve deep into individual players’ performance – by capturing things like several rebounds for example. This information can be later used as a coaching aid to improve player effectiveness.
This data can also be used for virtual gaming purposes where players from all over the world play on basketball courts in their respective locations. Here, embedded cameras help in capturing the video while smart hoops accurately measure the score.
In golf, sports analytics using embedded cameras involves two things:
- Tracking the trajectory of the ball
- Analyzing the movement of the club
Using embedded cameras is a no-brainer in the case of golf – especially amateur golf clubs and matches – since a very large area of land has to be covered. A one-time installation of multiple cameras can save thousands of dollars per year in comparison to using professional cameras to collect the same amount of data for detailed analytics. Camera-based sports analytics systems in golf use a combination of ordinary and depth cameras (or radars in some cases) to track and measure metrics like the angle of swing, ball speed, distance traveled, etc. These devices are used for both on-field measurements as well as for offering virtual gaming experiences.
Though not as popular as some of the other games on the list, cricket also extensively uses sports analytics to help teams and players improve the game. Cameras are used to track ball trajectories, capture bat swing patterns, measure timing, etc. While some of these actions are carried out using professional cameras, embedded cameras are finding their way into them owing to advantages such as low cost and ease of operation.
In American football, embedded cameras are used to track player speeds, receiver separation, acceleration, etc.
They can also help in analyzing the opposition team’s playing pattern through formation tracking, route recognition, and defensive coverage breakdown.
Sabermetrics is the term used to describe sports analytics in baseball. It involves all types of analyses related to a baseball game. When it comes to using embedded vision, cameras are used for collecting insights on batting and pitching patterns. These data points help in predicting match outcomes and individual player performances.
The NHL has been an active explorer of new technologies in the field of sports analytics – so much so that the League won the Alpha Award for Best Sports Innovation at the 2022 MIT Sloan Sports Analytics Conference for its player and puck-tracking technology (source: NHL).
In a sport where player and puck movements are very critical, embedded vision cameras – with the help of computer vision and machine learning – have made it possible to meticulously track each player.
A combination of visible light and infrared cameras is used in Ice hockey to generate data points for teams and coaches to derive insights into team and player characteristics.
TechNexion: Building High-end Cameras for Sports Analytics
TechNexion has ruled the embedded systems space for two decades now. With solutions spanning embedded cameras, system on modules, panel computing systems, etc., TechNexion has shown its presence across multiple domains in the embedded space.
One of our focus areas has been building camera systems for modern applications. Given that sports analytics has seen wide adoption of cameras in recent years, our R&D team has developed cameras that can be used for all kinds of sports and games. These include high-resolution 4K cameras, infrared cameras, and multi-camera systems. Check out all our camera solutions here.
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