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Solutions Built with Computer Vision

Autonomous driving, video surveillance, and biometric identification are some of the widely known applications of computer vision technology. Applying neural networks and deep learning techniques, we can build highly advanced visual recognition systems that solve many business challenges.

Solutions Built with Computer Vision

Computer Vision Technology in Industries

Computer Vision Technology
Computer Vision in Retail

People counting with the aid of machine learning algorithms can be useful to retailers in assessing store’s success and improving footfall. Tracking human movement in stores can also provide insight into shopper engagement and store layout optimization. Loitering or accessing off-limit areas can be flagged to prevent theft and other unauthorized activities.

Using computer vision to count the shoppers in a waiting line and alert when a threshold is reached enables retail staff to open new counters and manage checkout faster. Self-checkout systems with computer vision-powered cameras automatically recognize products and process them, improving both customer experience and security while speeding up the whole process.

Healthcare Applications of Computer Vision

Computer vision technology helps to minimize false positives and avoid needless procedures and treatments. Trained machine learning algorithms can classify cells and detect even the slightest presence of a condition, contributing to medical diagnosis with high levels of precision. Disease identification and early detection with the help of image recognition has significantly improved cancer prognosis.

Medical robots capable of high-definition 3D imaging aid accurate depth perception in minimally invasive procedures. Mechanical arms fitted with surgical instruments and cameras can be operated by doctors via a console where they view the magnified surgical site.

Computer Vision in Manufacturing

AI-powered defect detection systems gather data in real-time and compare it with predefined quality standards to identify defects and ensure an error-free production line. With computer vision-based systems constantly monitoring the manufacturing environment and equipment based on various metrics, maintenance can also be carried out proactively.

Adherence to safety standards can be strictly enforced using computer vision technology which detects even minor compliance violations and raises alerts. Intelligent monitoring systems also facilitate inspection of remote assets and work sites without compromising workers’ safety.

Sports and Computer Vision

Computer vision technology can be used to detect complex events like bad tackle or foul play in real time during a game. Camera-based systems can support referees in determining whether a goal has been scored or not.

Player pose tracking with AI vision can be used to detect the style of an athlete and when combined with data from wearables can help in assessing their performance. Stroke recognition applications that are capable of detecting and classifying strokes aid coaches and players to analyze games and improve skills.

Computer Vision in Transportation

Computer vision has been long employed in vehicle classification and there are specialized deep learning-based solutions for safety monitoring and productivity assessment in construction vehicles.

The technology is also used by law enforcement agencies to automatically detect rule violations such as speeding, illegal turns, and wrong-way driving. Other applications include autonomous navigation, detection of parking lot occupancy, traffic analysis, road condition monitoring, pedestrian detection, and collision avoidance systems.

Computer Vision in Retail

People counting with the aid of machine learning algorithms can be useful to retailers in assessing store’s success and improving footfall. Tracking human movement in stores can also provide insight into shopper engagement and store layout optimization. Loitering or accessing off-limit areas can be flagged to prevent theft and other unauthorized activities.

Using computer vision to count the shoppers in a waiting line and alert when a threshold is reached enables retail staff to open new counters and manage checkout faster. Self-checkout systems with computer vision-powered cameras automatically recognize products and process them, improving both customer experience and security while speeding up the whole process.

Healthcare Applications of Computer Vision

Computer vision technology helps to minimize false positives and avoid needless procedures and treatments. Trained machine learning algorithms can classify cells and detect even the slightest presence of a condition, contributing to medical diagnosis with high levels of precision. Disease identification and early detection with the help of image recognition has significantly improved cancer prognosis.

Medical robots capable of high-definition 3D imaging aid accurate depth perception in minimally invasive procedures. Mechanical arms fitted with surgical instruments and cameras can be operated by doctors via a console where they view the magnified surgical site.

Computer Vision in Manufacturing

AI-powered defect detection systems gather data in real-time and compare it with predefined quality standards to identify defects and ensure an error-free production line. With computer vision-based systems constantly monitoring the manufacturing environment and equipment based on various metrics, maintenance can also be carried out proactively.

Adherence to safety standards can be strictly enforced using computer vision technology which detects even minor compliance violations and raises alerts. Intelligent monitoring systems also facilitate inspection of remote assets and work sites without compromising workers’ safety.

Sports and Computer Vision

Computer vision technology can be used to detect complex events like bad tackle or foul play in real time during a game. Camera-based systems can support referees in determining whether a goal has been scored or not.

Player pose tracking with AI vision can be used to detect the style of an athlete and when combined with data from wearables can help in assessing their performance. Stroke recognition applications that are capable of detecting and classifying strokes aid coaches and players to analyze games and improve skills.

Computer Vision in Transportation

Computer vision has been long employed in vehicle classification and there are specialized deep learning-based solutions for safety monitoring and productivity assessment in construction vehicles.

The technology is also used by law enforcement agencies to automatically detect rule violations such as speeding, illegal turns, and wrong-way driving. Other applications include autonomous navigation, detection of parking lot occupancy, traffic analysis, road condition monitoring, pedestrian detection, and collision avoidance systems.

How Computer Vision Applications Work

How Computer Vision Applications Work

Computer vision applications run on sophisticated AI software that can perform a diverse set of tasks. The most frequent one involves image or video recognition by determining the different objects in the image/video.

Classifiers in computer vision mostly use convolutional neural networks (CNNs) to classify an image into one of the predefined categories. Locating an object in an image involves defining a bounding box to enclose the object. When classification and localization are repeated for all objects in the image, object detection is complete.

Object identification involves finding all instances of an object and specifying their location in the image. The process of following specific objects of interest in a given video results in object tracking. Instance segmentation is another task central to computer vision, which creates an accurate mask for each detected object. This technique is used, for instance, in blurring out children’s faces to protect their privacy.

A general approach to building a machine learning model for computer vision involves creating a dataset of annotated images and extracting features relevant to the problem being solved. The CNN model is then trained on the extracted features to solve the problem. We can evaluate the accuracy of the trained model using images that were not used for training.

Resources

Work with our AI / ML team to adopt computer vision technology in your business context.