The object recognition system based on AI (neural networks) will allow you to recognize different objects, like faces, goods, and other objects, and determine their belonging, count, form a black-and-white list, and control access to the system.
Recognition systems based on trained neural networks are now solving tasks that were previously available only to humans. It has become increasingly popular and effective in a wide range of tasks, from image and video analysis to autonomous vehicles and robotics. The use of various networks will allow you to automate many processes. These systems can learn from large amounts of data and improve their accuracy over time, making them useful for a wide variety of businesses.
The system face recognition and other parameters:
It will help you to accurately and quickly search for faces in the database of saved persons, and determine gender, age, and emotions. This function can be used to search for thieves and other intruders in stores. You will also be able to search through the list of “allowed” persons or the “white list" in fitness halls, restaurants, and other establishments to identify the guest. The same function of the “whitelist” is also used to account for staff working hours (“tabulation"). It will help you to reduce the cost of accounting and increase its accuracy. Also, using API for connecting external software solutions, it will allow you to integrate this solution into almost any software package you need.
Tracking people:
This type of neural network identifies a person in the video, not just a face. You can use it when calculating the number of visitors in various areas of the store, warehouse, or production. Also, this type of tracking will allow you to “lead” a person from camera to camera, tracking the number of people in certain zones. You can also use it to control the length of the queue, which allows employees to use their time more efficiently by automatically redirecting their time from work at the checkout to other areas of the store.
Recognition of objects and counting their number:
Using specially prepared datasets, you can train the recognition of almost any object of the product group and use quantity counting. This can be used in retail, to determine the quantity of goods on the shelves and the need to take the goods out of the warehouse before they are counted at the checkout. You can also apply this in a warehouse in the WMS system for an automated inventory process using drones.
Recognition of text groups - price tags, labels:
If you have several text objects with a complex text arrangement, with the context of the surrounding text relative to the neighboring one, this system will be a huge help in organizing these items, like price tags in stores. Price tag automation recognition and price control issues are solved within the framework of the “Mystery customer” solution. It supports mobile applications, with tasks for products or groups of products that need to be monitored.
You can take photos of the right price tags quickly. The photos are transmitted to the server, where trained neural networks recognize fonts of different types, make a comparison of abbreviated product names with their own SKUs, receive wholesale/retail prices, signs of promotional goods, and more. Thus, in a few minutes after sending photos to the server, you will get a report with data about the competitor's prices in the ERP system, and using our TMS system, you will be able to control the movement of an employee through competitors' stores.
Measurement of dimensions by video:
This solution uses the effect of stereo vision, similar to how a person determines the size. Only a neural network can determine in milliseconds the size of boxes with an accuracy of 1 mm. The size of the boxes can be from 5 mm to 2 meters or more. This solution can be used in warehouses or self-service points of postal companies or distributors /marketplaces to ease the working processes.
Voice platform:
The generation of voice commands from text (text-to-speech) and text recognition from voice messages (speech-to-text) will become important elements in simplifying employee communication with the system. You can use this platform for warehouse workers in WMS, for employees in the retail sales area, and for other areas of your interest.
Object recognition systems based on machine learning have become increasingly important in many different industries and systems. These systems continue to improve and become more accurate, they have the potential to revolutionize the way we can set interactions and workloads.
Our face recognition system will allow you to build a “digital impression” or “face descriptors” based on the existing database of persons you added to the system. Subsequently, the online system finds faces in the video stream coming from the cameras, builds “face descriptors” and searches for similar ones in the database of existing persons.
The system can be used both to search for undesirable persons (blacklist, list of scammers) and the reverse option (whitelist, list of employees). The system can be used in any process where biometric identification is required without physical contact and is important for transportation authorities to develop clear policies and guidelines.
The facial recognition module is used to improve security by accurately identifying people and objects that are attempting to gain access to your facility or area. Then you can decide to allow access, often with the help of third-party systems that are called through the API. This can be an ACMS (Access Control and Management System), a timesheet system, etc.
Uses a facial recognition module and tracks the movement of people by counting unique visitors. This module can be used to automatically record the number of visitors in different parts of the store or other objects, control the size of the queue at the checkout, etc. It will benefit you as a property owner or your managers, improving security, resource utilization, customer experience, operational efficiency, and compliance.
The object recognition module and counting the number of objects can be used for automatic quality control of the location of goods on the shelves or to generate an event of the need to replenish stocks of goods on the shelf.
You can do this both from a camera installed on the trading floor and from a camera on a cleaning robot. This will identify the condition of the goods on the shelves and promptly inform about the need to replenish the shelf right after the goods have been picked by customers.
Unlike the number recognition systems built into smart cameras, this module can be quickly trained on numbers from different countries, thus offering significant benefits to your organization by bringing enhanced security, improved traffic management, cost savings, automation, customization, scalability, and data analytics by identifying suspicious or unauthorized vehicles in real-time.
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