Most Popular Computer Vision Applications


Most Popular Computer Vision Applications

Computer vision is a sector of artificial intelligence which uses machine and deep learning to allow computers to “see” and analyze their surroundings. Computer vision has a massive impact on companies of all industries, from retail to agriculture. It is especially useful for problems where we would need a human’s eye to view the situation. Because of the broad amount of problems that exist in that criteria, thousands of applications of computer vision have not been discovered or exhausted yet.

This article will be a cumulative list post of fast-growing and progressive computer vision applications used by prominent industries in 2021.

Computer Vision in Sports

Sports Production. Fully automated sports production has been created through deep learning, including zoom ins and pan-outs identical to professional, human-led production. Instead of using cameramen, computer vision is being utilized to recognize positions of players and the ball in order to focus specifically on those aspects depending on what is in the view.

Player Tracking. Videos, or even live footage, is programmed to be understood frame by frame. Player actions can be deciphered by recognizing patterns between human body movement and pose between frames. This allows to analyze athlete performance, exercise progress or review athlete technique.

Example of Player Tracking With Deep Learning Based Pose Estimation

Ball Tracking. Ball tracking is an application of deep and machine learning that makes the ball appear visible on screen. This is useful because being able to visibly see the ball in sports with large fields (ex. Football) makes news reporting easier for sports newscasters. Additionally, ball tracking enables tactical analysis of games and teams.

Computer Vision in Health and Medicine

Cancer Detection. Machine learning is incorporated in medical industries for purposes such as breast and skin cancer detection. Image detection allows scientists to pick out slight differences between cancerous and non-cancerous images, and diagnose data from magnetic resonance imaging (MRI) scans and inputted photos as malignant or benign.

Cell Classification. In 2016, ML was used to classify T-lymphocytes against colon cancer epithelial cells with high accuracy. ML is expected to significantly accelerate the process of disease identification regarding colon cancer efficiently and at little to no cost post-creation.

Movement Analysis. Neurological and musculoskeletal diseases such as oncoming strokes, balance and gait problems can be detected using deep learning models and computer vision even without doctor analysis. Pose Estimation computer vision applications that analyze patient movement assist doctors in diagnosing a patient with ease and increased accuracy.

Mask Detection. Companies such as Uber have created computer vision features to be implemented in their mobile apps to detect whether passengers are wearing masks or not. Programs like this make public transportation safer during the pandemic.

Tumor Detection. Brain tumors can be seen in MRI scans and are often detected using deep neural networks. Tumor detection software utilizing deep learning are crucial to the medical industry because they can detect tumors at a high accuracy to help doctors make their diagnoses. New methods are constantly being developed to heighten the accuracy of these diagnoses.

Computer Vision in Agriculture and Farming

Defects in Agriculture. Damaged produce can be detected while it is in processing using machine learning algorithms. Algorithms are given multitudes of data and are trained to recognize differences between ripe and spoiled produce.

Counting. Masses of produce can be fed through an imaging system which then can count how many objects are in the scene. This allows farmers to have data on how much they are farming and thus allows them to calculate how much they should charge for the produce.

Plant Recognition. Programs can be used to identify plants and animals at the species level from a photo inputted by the user. Farmers can now easily pinpoint weeds and pests with this computer vision application.

Animal Monitoring. Animals can be monitored using novel techniques that have been trained to detect the type of animal and its actions. There is much use for animal monitoring in farming, where livestock can be monitored remotely for disease detection, changes in behavior, or giving birth. Additionally, agriculture and wildlife scientists can view wild animals safely at a distance.

Farm Automation. Technologies such as harvest, seeding, and weeding robots, autonomous tractors, and drones to monitor farm conditions and apply fertilizers can maximize produce with labor shortages. Agriculture can also be more profitable when the ecological footprint of farming is minimized.

Agriculture Computer Vision Application for Animal Monitoring
Computer Vision in Transportation

Autonomous Driving. Otherwise known as self-driving cars, autonomous vehicles allow users to never have to touch a steering wheel during their commute. Tesla is the leading computer vision company which makes breakthroughs in autonomous driving, and has nearly made the idea of completely self-driving cars come to life. This may eventually be safer than having humans drive our own cars.

Driver Attentiveness Detection. Deep and machine learning algorithms that have been given thousands of pieces of data of attentive vs. inattentive faces can detect differences between eyes that are focused and unfocused, as well as tell-tale signs of driving under the influence. Artificial intelligence in this context is important because it has the potential to protect both the driver and other drivers in the vicinity. Its results may also have the ability to be used by authorities as evidence of intoxicated driving.

Number Plate Recognition. To produce vehicle location data, automatic number-plate recognition implements character recognition on images of vehicle registration plates. This technology can be incorporated in rule-enforcing cameras on the road, electronic toll collection, or other law enforcement purposes like criminal investigations.

Traffic Analytics and Safety. Computer vision applications in traffic analytics detect the numbers on a license plate caught speeding, running a light, and more. That data can then be reported and automatically assigned fines and other repercussions without administrative interference. Other data collected through traffic analytics can help road planning and traffic counting as well.

Collision Avoidance. Deep neural networks have been used recently to investigate deep learning and the use of it for autonomous collision avoidance systems. Vehicles can detect when they are within a certain amount of distance from a collision, or if an oncoming car is going fast and riskily enough to cause one.

Dead Angle Detection. The side scanning system recognizes cyclists, vehicles or persons who are next to the vehicle and signals them by means of an LED or warns additionally by an alarm sound if someone is in the danger area.

Computer Vision Application for Vehicle Counting

Computer Vision in Retail and Manufacturing

Customer Tracking. Strategically placed counting devices throughout a retail store can gather data through machine learning processes about where customers spend their time, and for how long. Customer analytics can improve retail stores’ understanding of consumer interaction and improve store layout optimization.

People Counting. Computer Vision algorithms are trained with data examples to detect humans and count them as they are detected. Such people counting technology is useful for stores to collect data about their stores’ success and can also be applied in situations regarding COVID-19 where a limited number of people are allowed in a store at once.

Theft Detection. Retailers can detect suspicious behavior such as loitering or accessing areas which are off limits using computer vision algorithms which are autonomously analyzing the scene.

Waiting Time Analytics. To prevent impatient customers and endless waiting lines, retailers are implementing queue detection technology. Queue detection uses cameras to track and count the number of shoppers in a line. Once a threshold of customers has been reached, the system sounds an alert for clerks to open new checkouts.

Social Distance. To ensure safety precautions are being followed, companies are using distance detectors. A camera tracks employee or customer movement and uses depth sensors to assess the distance between them. Depending on their position, the system draws a red or green circle around the person.

Productivity Analytics. Productivity analytics track the impact of workplace change, how employees spend their time and resources, and implement various tools. Such data can provide valuable insight into time management, workplace collaboration, and employee productivity.

Quality Management. Quality management systems ensure an organization reaches customer’s requirements by addressing its policies, procedures, instructions, internal processes to reach an overall consumer satisfaction rate.

What’s Next?

Deep and machine learning technology has been used to create computer vision applications in dozens of ways and for industries of all types.