From medical diagnostics to entertainment, this insightful article dives into popular examples of computer vision in 2023. Here you will discover the most cutting-edge uses for computer vision (CV) systems and uncover high-level projects and ideas currently in market demand. In addition, you’ll gain an understanding of the fundamental basics behind CV as well as real-world use cases for it.
What Is Computer Vision?
Okay, so you might be asking: “What the heck even is computer vision??”
Well.. I’m glad you asked.
Computer vision is an extraordinary technology, merged with artificial intelligence (AI) and machine learning, that empowers computers to “see” objects in photos, videos or in real time.
Similar to how humans can see.
This form of computational vision is quickly becoming the norm for AI vision inspection and remote monitoring automation.
The potential applications reach across various industries; from retail to security, healthcare, construction, automotive manufacturing & logistics – even agriculture.
What are the real life applications of computer vision?
1. Self-Driving Cars
The era of autonomous car driving is upon us.
Market leaders including Tesla make significant progress through innovative technologies like 3D imaging and 5G.
Tesla autonomous vehicles use cameras that analyse the environment and allows users access to additional functions, including autopilots.
These cars also uses 360-degree image sensors that recognize objects with computer graphics.
This allows users to operate autonomously by driving their car manually or by providing an auto decision-making function for their vehicle.
Utilizing computer vision in self-driving cars provides a plethora of advantages.
For example, with the help of this technology, an autonomous vehicle can identify hindrances on its route and take evasive action accordingly.
2. Facial recognition
While facial identification has become commonplace for individuals using smartphones, public security has also been a notable market player for facial detection technologies.
Recognising faces in public has been used for some jurisdictions but is currently banned in some others.
The ability to detect faces can be enhanced with advanced machine learning and advanced vision systems.
The most common facial recognition system in public use combines image analysis and pattern recognition algorithms.
3. Disease Progression
Computer vision offers a unique solution for understanding the progression of diseases, from recognizing abnormalities in tissue structure to tracking the effectiveness of treatments.
It draws data from medical histories, symptoms and other sources to provide an accurate diagnosis quickly compared with manual methods.
By identifying patterns within datasets through predictive analysis capabilities it allows earlier detection than ever before.
Medical professionals are now provided with invaluable evidence-based decisions on how best to manage their patients’ health due to this revolutionary technology.
4. Cancer Detection
Medical industries are leveraging computer vision and machine learning to detect breast and skin cancer, making use of image recognition techniques that allow scientists to distinguish between healthy tissues and malignant ones.
For instance, a combination of MRI scans as well as input photos can be used by ML-powered models to diagnose data with an accuracy akin to professional radiologists.
5. Movement Analysis
Neurological and musculoskeletal conditions are easily detected using deep neural models and computer vision.
Pose estimation computers analyze patient motion and help doctors identify patient ailments faster.
6. Medical Skill Training
Advanced computer vision technologies are utilized to evaluate the abilities of expert learners on self-learning platforms.
For example, simulation-based surgical training systems have implemented this technology in order to assess their students’ performances and provide meaningful feedback.
This allows individuals to identify areas that need improvement, thus enabling them to enhance their skill set as they continue learning.
7. Quality Management
Smart cameras provide scalable ways to automate visual inspections and quality assurance in factory production line operations.
This is achieved through using deep learning models enabling realtime object discovery to produce detection results that are often times much better than when manually performed by a person.
AI inspection is highly robust and uses machine learning techniques that do not require expensive special equipment or inflexible settings.
Thus, computer vision technologies have highly scalable quality assurance applications.
8. Intelligent video analytics
Using artificial intelligence models, video analytics systems detect suspicious activity and warn staff, enabling them to take action.
Detection of theft using computers has proved much more accurate than human vision due to its ability to analyze movements in real-time.
AI cameras may also help detect other behavior such as assault and accidents, improving safety.
9. Parking Occupancy Detection
With the mission of parking lot occupancy detection, visual parking space monitoring is being used in smart cities all over.
Utilizing computer vision applications and a deep Convolutional Neural Network (CNN), these decentralized solutions provide efficient results.
The major benefits to utilizing these camera-based systems include scalability for large use, low cost maintenance and installation since existing security cameras can be re-used.
It also deters theft and utlizes facial recognition in the apprehension of criminals who might be looking to steal something from your car.
10. Traffic Flow Analysis
Utilizing computer vision to monitor traffic flow can be a powerful tool for providing an in-depth view into real-time conditions.
From detecting and classifying obstacles, determining lane closures and merge points, analyzing crosswalks, identifying bottlenecks – these features allow us to take preventive measures against any irregularities on the road.
Moreover, this technology can also detect hazardous situations such as accidents and alert authorities right away; making it invaluable for ensuring safety while improving traffic flow simultaneously.
11. Crop and Yield Monitoring
Traditionally crops are monitored using subjective human judgment and opinions and is often times not timely or accurate.
Computer vision provides continuous monitoring of plants’ growth and detection of crop changes caused by malnutrition or disease.
Technological advances in computer vision field visualization improve the process of determining production.
This can lead to lower prices for you when you go to buy groceries.
12. Pedestrian Detection
Computer vision can be utilized to raise safety levels exponentially by accurately recognizing and tracking pedestrians in real-time.
Changes in body language, posture, position, and movement can alert authorities of any potential threats or suspicious behaviour that could occur.
Moreover, computer vision identifies the presence of hazardous items near individuals such as bags or weapons that may need further investigation.
In short: computer vision could prove to be an invaluable tool for enhancing pedestrian safety if properly implemented.
13. Observation of Moving Objects
Computer vision technology is making a remarkable impact by detecting and monitoring motion in real-time.
Not only can it identify any anomalous behavior, but also recognize potentially hazardous activities like someone doing something prohibited in an off-limits area.
Furthermore, applying computer vision technology has aided conservation efforts immensely as researchers are now able to observe wildlife closely and determine their natural behaviors better than ever before.
14. Insect Detection
Rapid and accurate detection of flying bugs is important, especially in pest control.
Traditional identification and counting of fly insects is inefficient and labor intensive.
Using vision-based systems, flying insects can be counted or recognised, which can have a positive impact on agricultural output.
15. Livestock Health Monitoring
Computer vision technology can monitor animals remotely using image and video data in realtime.
It can also be useful in counting animals, detection of diseases, unusual behaviour, or monitoring births.
Using video images and image files, AI-powered software can provide farmers with information about food and water availability as well.
16. Road Condition Monitoring
By leveraging computer vision, road conditions and any potential hazards can be effectively monitored.
Whether it’s recognizing potholes or measuring distances to assess changes on the roads, this data is key in informing authorities or drivers about unpredictable elements that may be present.
Moreover, with modern computer vision technology at our disposal, speed detection and traffic control become more efficient – leading to greater safety for all travelers; from cyclists to pedestrians.
17. Irrigation Management
Computer vision can be utilized to optimize irrigation systems, by spotting obstructions or cracks in pipes as well as detecting various types of crop diseases.
This data can then assist farmers in deciding which areas need extra water and how quickly their crops are adapting to shifting circumstances.
In addition, computer vision allows for the measurement of moisture levels inside the soil; granting producers greater insights into exactly what their crops require for optimal growth.
18. Human Pose Tracking
Computer vision technology has the power to offer precise insights into body movements in any environment.
By monitoring posture and position shifts, computer vision technology can alert us to physical injuries or ailments.
It also can recognize facial expressions that could signify security or health threats such as strokes – a computer vision application can prove to be a trusted companion for tracking human behavior.
This data can also be harnessed effectively for sports activities such as yoga practice, exercise sessions and sports training programs; enabling customers with more accurate feedback on their performance.
19. Vehicle Classification
Computer vision permits vehicles to be accurately identified and categorized, which can provide a great deal of assistance with traffic or surveillance activities.
For instance, this technology can quickly detect ambulances or police cars–which is invaluable information in certain scenarios.
In addition to that valuable capability, computer vision also has the ability to differentiate between auto classes such as luxury sedans or small SUVs; this could prove extremely useful when determining parking fees and managing access permissions for restricted locations.
20. Highlight Generation
With the power of computer vision, broadcasters and fans are rewarded with a more entertaining sports-watching experience.
Through advanced image recognition algorithms, computers can detect key points in any game – like goals scored, free kicks taken or yellow cards issued – and quickly generate highlights as such.
This way you get to relive all the best moments right away without having to watch an entire match.
Furthermore, computer vision can also generate statistical videos which map out each player’s progress over an entire season or tournament – providing coaches and scouts with the capacity to monitor players’ development more rapidly and precisely.
21. Automated License Plate Recognition (ALPR)
Automated License Plate Recognition (ALPR) technology leverages computer vision to decode and recognize vehicle license plates.
Not only does ALPR enable improved road safety, but it’s also a powerful tool for tracking down stolen vehicles faster.
Moreover, this system allows law enforcement agencies to quickly identify potential suspects in criminal cases with its accurate scanning of matching plate numbers.
Furthermore, with Automated License Plate Recognition (ALPR), drivers can effortlessly enter restricted areas while applications like access control and parking management make their job even easier by eliminating the need to manually input their license plate information.
Ever heard of someone getting a ticket for not paying a toll?
You can thank ALPR.
22. People Counting
Computer Vision algorithms use data examples to identify humans and accurately count them.
This people counting technology is a valuable tool for retailers as it helps them collect important metrics about their business, while also being applicable in times of COVID-19 where only a finite number of individuals are permitted inside stores simultaneously.
23. Customer Tracking
Computer vision offers retailers a glimpse into customer behavior, giving them the ability to track customers in-store while they browse.
Through images of shoppers, store owners can learn which areas patrons spend more time in and what products grab their attention.
Using this data allows retailers to fine-tune their operations by creating displays that are both attractive and effortless for consumers to navigate – improving the overall shopping experience.
Additionally, computer vision can be utilized for facial recognition and other security purposes to help store owners trace any possible theft of products.
Thanks to this cutting-edge technology, businesses are able to create a safer environment as well as enhance the shopping experience of their patrons.
24. Performance Assessment
Computer vision is a potent tool to assist coaches and teachers in assessing the performance of their students or athletes.
By leveraging multiple cameras and sensors, this system can track body movements in real time with remarkable precision, affording valuable insight into any shortcomings present.
This data can then be employed to deliver helpful advice on how to make improvements and garner superior results.
Moreover, a computer vision system also offers the benefit of tracking progress over time for both instructors and coaches so that they may assess the efficacy of their teaching methods.
25. Waiting Time Analytics
Retailers use queue-detecting software and other techniques in order to prevent impatient customer queues.
Queue tracking uses camera systems to track and count shoppers in one line.
Once customers have reached the threshold, it alerts the clerks to open the next checkout.
Computer vision technologies are also being implemented to ease the burden of travel by evaluating and improving wait times at airports.
As someone who has been delayed at an airport more times than he can count, these solutions to reduce delays at airports cannot come quick enough.
26. Sports Team Analysis
Computer vision technology can revolutionize the way sports teams train and strategize.
With sophisticated algorithms, computers are able to track individual player movements in real-time – enabling coaches and analysts to review a match quickly and pinpoint any flaws in their team’s methods.
This allows for more informed decisions during gameplay which could ultimately give them an edge over their opponents.
In addition, computer vision can also equip teams with exceptional scouting abilities by providing them the capability to quickly collect information on opposing players ahead of a game.
27. Ball Tracking
Computer vision is a powerful tool for coaches and players alike, providing them with real-time insights into the trajectory of balls in sports such as soccer and basketball.
Predictions on where exactly the ball will land can be made quickly and accurately, leading to fewer errors during passing or shooting plays.
Moreover, computer vision also enables teams to measure speed and angles precisely – making it simpler to hone technique no matter what level you’re playing at.
28. Goal-Line Technology
Leveraging computer vision, goal line technology accurately and confidently identifies when a ball has crossed the net.
This means no more questionable calls or decisions – players, coaches, referees and spectators can all be certain of each goal’s legitimacy.
Furthermore, video replays become invaluable for analysis and practice; teams may assess their defensive frailties on-the-fly in order to sharpen up their tactics moving forward.
With state-of-the art camera systems powered by smart image processing algorithms at its core, this technology provides soccer with unprecedented insight into game play.
Hopefully this will result in less bad calls by the refs!
29. Real-Time Coaching
Computer vision technology is revolutionizing the world of sports coaching, making it easier for players to receive timely feedback and make improvements.
From golf and tennis to a range of other activities, computer vision algorithms can quickly identify mistakes in player’s movements; providing coaches with instant insight on what needs correction or improvement.
Moreover, computer vision helps ensure that drills are performed properly by immediately offering guidance when errors occur – leading to greater success as athletes hone their skills at an accelerated rate.
By doing this, gamers can concentrate on honing the right technique instead of being concerned about performing each drill to perfection.
What are the applications of computer vision in security?
Visual surveillance utilizes computer vision to monitor road signs, traffic control equipment and other intelligent transportation systems and facilities to ensure that safety of public places and critical infrastructure are maintained.
Computer vision applications in security are expected to increase significantly in 2023, with facial recognition systems being used for access control, object detections systems used to monitor restricted areas, and automated surveillance solutions providing enhanced monitoring capabilities.
What is computer vision in AI?
Computer vision in AI is the ability for a computer system to interpret images, videos and 3D data in order to gain insights about the environment it’s perceiving.
It is commonly used for tasks such as facial recognition, object detection, tracking, AI tools, and automated surveillance.
What is the best computer vision software?
There really is no “best” computer vision software, with each having their own strengths and weaknesses.
In 2023, the market for computer vision software is overflowing with several powerful solutions for any application.
Microsoft Azure’s Computer Vision Toolkit, Google Cloud Vision API, Amazon Rekognition, OpenCV and Clarifai are only some of the popular selections.
These all offer unique features to fulfill your needs while ranging in complexity to suit every ability level.
Is computer vision the same as artificial intelligence?
Artificial Intelligence (AI) is an expansive term that describes computer systems which learn from their environment and transform over time, taking actions based on the gathered data.
Conversely, computer vision, a specific subset of AI, focuses primarily on studying images or videos.
Through algorithmic analysis in visual components such as facial features and 3D objects to texture, motion and color – computer vision can be used for various applications such as fraud detection & face recognition software.
While AI can handle any kind of inputted data; computer vision emphasizes examining visuals alone – thus making it perfectly suited for profound image-based tasks.
What are the advantages of computer vision?
Computer vision is extremely advantageous, as it ensures greater accuracy and precision in automated tasks while improving safety and security.
With computer vision implemented, you can be rest assured that potential hazards or intruders will be immediately spotted with perfect accuracy – a human eye alone just cannot compare.
Just ask any police department about witness statements: you will usually get a different story from everyone depending on their perspective and individual circumstances.
Additionally, tedious manual processes are now strippable when computerized visuals take over; not only does this save time and money but the results presented surpass any that would have been achieved by manual labor.
Is computer vision a good career?
Computer vision engineering’s employment prospects are very positive.
Computer science jobs are expected to increase 15% over the period 2020-2029.
What is the work of computer vision?
The multiple benefits of using computer vision are undeniable: from enhanced accuracy and precision to improved safety and security, increased efficiency in operations as well as cost savings.
Its immense potential stretches even further than what the human eye can capture; it is capable of detecting objects for timely decisions that ultimately result in more accurate outcomes.
Computer vision also eliminates tedious manual processes by automating them, leading not only to time-saving but money-saving opportunities with far better overall results than those produced by a person manually inputting data.
What are the examples of computer vision in real life?
- Facial recognition for safety and authentication.
- Object detection and identification in self-driving cars.
- Intelligent detection and motion tracking for immersive gaming experiences.
- Medical analysis through image processing.
- Automated quality control during factory production.
How to get a job in computer vision?
If you’re looking for a career in the computer vision field, there are a number of paths that you can take.
To get started, obtain an educational background in Computer Science or Mathematics, and use your skills to work on coding projects and machine learning libraries to detect patterns in visual data.
You may also want to consider taking certifications or participating in professional training programs as these will give you an advantage when it comes time to apply for jobs related to this industry.
With dedication and hard work, anyone has the potential of succeeding in this growing field.
Are computer vision and image processing same in AI?
Computer vision and image processing are different, but interconnected disciplines within artificial intelligence.
Whereas computer vision aims to teach computers how to recognize patterns in digital images, image processing emphasizes the algorithmic alteration of these same photographs.
As a demonstration – while computer vision could be used to distinguish objects inside an image, image processing is better suited to improve or suppress particular features or hues.
Moreover, machine learning algorithms can often further refine the data collected by computer vision systems..
Is computer vision a part of machine learning?
Absolutely, computer vision is an essential component of machine learning.
It involves utilizing algorithms to process data and uncover patterns within it.
By leveraging these powerful techniques in combination with image-based data, we can learn how to detect objects from images, recognize faces or even generate new images based on existing datasets – all by using visual inputs by means of computer vision.
With this technology at our disposal, any visual input or dataset has the potential for offering valuable insights that could transform a business or organization’s success.
What is visual artificial intelligence?
Visual artificial intelligence (AI) offers a range of possibilities with its sophisticated computer vision algorithms, from recognizing objects and separating images to facial recognition.
Thanks to these capabilities, AI can power autonomous robots in their environment by helping them detect independent obstacles and navigate around them properly.
Moreover, medical professionals are now able to draw valuable insights from medical imaging with the help of visual AI’s identifying features – enabling physicians to make quicker yet more informed conclusions from medical images.
What is the goal of computer vision?
Through the power of computer vision, machines are capable of processing and comprehending visual information – such as images and videos – in a meaningful manner.
By utilizing sophisticated algorithms, computers can be taught to not only recognize objects in photos but also spot faces accurately, segment an image into multiple sections effortlessly, generate new pictures through existing image data sources conveniently and much more.
This revolutionary technology can be utilized for a broad range of applications, with goals ranging from aiding medical experts in diagnosing diseases to autonomously navigating robots and self-driving cars on roads.
In conclusion, computer vision technology has revolutionized many industries and is playing a major role in today’s digital landscape.
Whether in the realms of medicine, autonomous vehicles, or sports teams and scouting, computer vision is enabling more accurate and efficient results than ever before.
As technology continues to improve and become more accessible, there is no doubt that computer vision applications will become increasingly popular across all industries.
Keep in mind that here we discussed the benefits of computer vision. Elsewhere we will discuss possible drawbacks and how to avoid them.
If your interested in learning more, check out some fun AI tools.