Get to know the applications of computer vision and facial recognition!
Computer vision and facial recognition are the branches of Artificial Intelligence. Using this the computers get the ability to think like humans.
What is computer vision?
Computer vision allows computers to extract information from videos, images, and other visual inputs. It then makes recommendations or acts based on that data. It uses data, cameras, and algorithms rather than optic nerves and retinas.
What is facial recognition?
Facial recognition is a way of verifying or recognizing a person’s identity to look at their face. It identifies people in videos, photos, or real-time using facial recognition systems. This technology is mainly used for law enforcement and security.
Applications of Computer vision and facial recognition:
People counting and customer tracking
Counting devices collect data about how long and where customers spend their time. Machine learning procedures help in this process. So, customer analytics can assist retailers in better understanding customer interactions which can optimize store layouts.
Data examples also help them to count and detect humans when it is seen visibly. Stores can use this technology to collect data about their success. It can also be used in pandemic situations where only a few people can gather at a time.
Ensuring theft detection, safety, and managing quality
Companies use distance detectors to ensure support to safety precautions. A camera monitors the movement of customers or employees. It also uses depth sensors to identify the distance between them. The system then draws a green or red circle around the person depending on their position.
A quality management system ensures that the organization meets the requirements of the customers. It does so by addressing procedures, policies, internal processes and instructions. This also ensures a great level of customer satisfaction.
It is using computer vision that identifies the scene. Retailers can detect suspicious behaviours like accessing off-limit or loitering areas.
Skill development and training
The minimization of assembly line operations in industrial production is the other application. Human action evaluation can help in the development of standardized action models. Moreover, this helps in several operation steps and also evaluates the trained profession’s performance.
The utilization of computer vision in automated vehicle classification consists of a long history. The technology for this classification has been improved over the years.
Vehicles can be classified, tracked, and detected in multiple lanes using affordable sensors. Moreover, LiDAR, CCTV cameras, and even thermal imaging devices assist with this. Combining all these sensors can improve vehicle classification accuracy.
Collision avoidance systems
Computer vision applications recognize and detect traffic signs. Vision techniques are used to classify and recognize traffic signs. They segment traffic signs from several traffic scenes. Moreover, deep learning algorithms classify and recognize traffic scenes.
The most advanced driver assistance systems include lane detection and vehicle detection. Deep neural networks were frequently used to research deep learning and its uses in autonomous collision avoidance systems.
Monitoring the state of the roads
There is a need to track asphalt and concrete civil infrastructure. Computer vision and facial recognition-based defect detection systems are being developed. Pavement condition assessment offers data that is used later on. It also helps to make consistent and cost-effective decisions about pavement network management.
Food quality gets determined by the quality and yield of significant crops such as wheat and rice. Monitoring of crop growth has relied on subjective human judgement. It is neither accurate nor timely. Response to nutrient requirements and plant growth gets monitored continuously using computer vision.
Also, computer vision can detect small changes in crops due to incorrect nutrition earlier. It then offers an accurate and reliable foundation for timely regulation. The growth of plants can also be measured using computer vision applications.
Plantations can be monitored regularly using image processing with drone images. It is also possible to identify which parts of the lands are fertile using these orthophotos. It also determines less fertile areas as well as these plantation fields that are entirely non-growing.
Flying insect counting and recognition must be done accurately and quickly for pest control. Counting and manual identification are time-consuming and inefficient. Flying insects can be recognized and tracked using vision-based systems.
Apart from the other thing, machine learning which is used in the medical field can detect cancers. Image recognition allows scientists to detect minor differences between non-cancerous and cancerous images. It can also diagnose uploaded photos as benign or malignant and also data from MRI scans.
In-depth neural networks frequently detect brain tumours which are seen only in MRI scans. This sort of tumour detection software is important to the medical industry. It can detect tumours with great accuracy and also helps doctors in making the diagnosis.
Disease progression mapping
Computer vision can determine critically ill patients and send them to the appropriate medical care. People affected by pandemic diseases have a faster rate of respiration.
In-depth cameras combined with deep learning detects abnormal respiratory patterns for various health issues patients. This allows for accurate large-scale screening.
Healthcare and rehabilitation
Physical therapy will be essential for athletes recovering from sports injuries and for stroke survivors. Moreover, the charges for supervision by a hospital and medical professionals are necessary obstacles.
People give more importance to home training with a vision based rehabilitation app. It allows them to practice movement training confidentially. Added to this, human action evaluation used in computer therapies lets patients train at home. So, it guides them to perform actions exactly and prevent further injuries.
Assessing training and skill development
Computer vision applications check the skill levels of expert learners on self-learning platforms. Surgical education has become advantageous from the development of simulation-based surgical training platforms.
This technique of assessing qualities allows for the enhancement of computational approaches. Moreover, individuals can receive beneficial feedback information that assists them to enhance their skills.
Thus, Computer vision and facial recognition are the simplest technical terms. But, teaching it to the computers is the most difficult job. So, it will be incredible to decide what will occur in the future with these technologies.