The times when CCTV systems were only used to monitor the necessary spaces or for perimeter security are long gone; today there are more effective tools available for more efficient and automatic evaluation of scenes. The system uses the potential of artificial intelligence, and we can solve almost any problem by training a deep neural network.

The application uses special deep neural networks for video analysis. Artificial intelligence is applied to video in various resolutions for early detection and recognition of phenomena of interest, from the violation of a perimeter by a person to complex directional detection or analysis of other objects and phenomena.

Basic network properties

  • neuron detection of objects of interest
  • includes persons, vehicles, trucks, buses and cyclists
  • high reliability thanks to the method of network training
  • markedly higher detection quality than with regular pixel analysis
  • supports detection in zones or when lines are crossed
  • support detection of directions and colours
  • may respond to the number of objects
  • may respond to the time spent in a zone
  • provides data for forensic searches



  • oncoming traffic detection
  • traffic congestion detection
  • level crossing monitoring


  • detection of suspicious persons
  • event in a scene with shadows and wind
  • network launch and detection with shadows and wind


  • counting of persons in one direction
  • counting of all vehicles and only trucks


  • no false alarm when the light changes
  • detection when a person’s position changes


  • monitoring of persons in a crisis environment – recognising potential risks (e.g. panic in a crowd)
  • tracking the movement of people (e.g. the exact routes of shoppers in stores can be determined with a video analysis)
  • monitoring of public transport stops


  • vehicle tracking
  • records of vehicles entering and exiting the grounds
  • automatic barrier lifting


  • monitoring the passage of vehicles through a one-way street and their direction of movement
  • warning signals, detection of traffic obstacles (e.g. passing through a tunnel, traffic congestion, collision, etc.)
  • obstacle detection for active and integrated security systems
  • parking lot occupancy monitoring, occupancy prediction
  • immediate signalling and subsequent safeguarding (e.g. level crossings)


  • fast and efficient solutions for shops, shopping centres, pharmacies and hospitals.
  • people counting and controlled access to buildings
  • affordable
  • easy installation
  • high accuracy (up to 98%)
  • use of AI
  • movement statistics
  • can be connected to an existing CCTV system
  • contactless control of the movement of people
  • can be used for advertising banners/messages




Artificial intelligence is a tool for advanced quality control or machine diagnostics that uses machine vision and deep neural networks; it is perfect for image, video, sound or vibration recognition. Neural networks are taught much like humans with examples (patterns) that they later use to respond to stimuli from the real world. Their advantage is the ability to generalize from the learned knowledge, which they can then apply in conditions different from those under which they were trained.
This allows the system to detect surface defects of materials on static photos or video, where dependencies in the development of the video can also be detected for quality control, selection of parts, or control of production processes. The system is also supplied as a complete unit with cameras, lighting and a computer for these purposes.  2D or 3D measurement of dimensions can also be added to the system.
The primary learning system prior to live deployment follows the patterns that the user indicates in the web browser, thus defining the types of defects to be detected. This core learning takes place off the target production line on a powerful server.
The system can subsequently continue to be taught while in operation by gradually adding new patterns. If a new defect occurs, the user simply marks the new images and lets the system retrain itself automatically. Everything operates on a strictly user basis while the production line is running without the need for programming.

Intelligent control

  • advanced quality control or machine diagnostics
  • ability to generalise from learned knowledge
  • the system detects surface defects of materials on static photos or video
  • the primary learning system prior to live deployment follows the patterns that the user indicates in the web browser, thus defining the types of defects to be detected
  • the system can continue to be taught by gradually adding new patterns





  1. Predictive failure detection and alarm generation are areas in which companies can save the most. If a production line is to be down, it can be much more cost-effective to shut it down early and perform maintenance than to shut it down in the middle of the production process. Predicting failures and generating alarms based on algorithms can save a lot of money.
  2. Process optimisation is another popular area. The system generally provides recommendations for setting setpoints and regulated quantities for systems. There are two main types of process optimisation: open-loop and closed-loop. Open-loop optimisation involves interaction with the user, where the system can recommend changes to optimise the process, and a technician or other expert reviews the recommendations and decides whether to implement them. Closed-loop optimisation completely eliminates human intervention and tuning recommendations are applied automatically.
  3. Anomaly detection detects deviations from normal operating conditions. This can show you when a process is not running optimally, or predict bad a poor production process or equipment errors. These systems typically provide a number that indicates how close the process is to normal operating conditions. The results can be thought of as another “sensor”, and the results of the anomaly detection algorithm can be used to do anything from alerting you to an abnormal condition to stopping a process. Although anomaly detection is similar to predictive failure detection and alarm generation, it shows you how a process is performing right now rather than predicting how it will perform in the future.
  4. Defects are usually analysed by image recognition algorithms; this analysis can be very useful for classifying parts and detecting abnormalities.

Advantages of a failure detection system

  • predictive failure detection and alarm generation
  • the system provides recommendations for setting setpoints and regulated quantities for systems
  • anomaly detection detects deviations from normal operating conditions
  • defects are usually analysed by algorithms


We can now increase the safety of workers in three internally tested scenarios. We are gradually deploying these solutions in pilot projects in industrial companies.

  • Machines stop before an accident happens. 

Our system can distinguish between the human body and processed materials, other machines, etc. If a human hand approaches a part of the machine that could cause an injury, we stop the machine. This is our original idea, which is why we completed this feature first. Conversations in industrial companies repeatedly pointed us to two other scenarios:

  • Authorisation of workers to operate equipment. 

The software can recognize individuals and allow (or not allow) them to operate a machine, or it can only make some of its functions available to certain people. Two companies voiced this requirement. One company wanted to only enable certified workers to operate a forklift, and the second company wanted their CNC machine to allow the service technician to open it while it is running and adjust it after his authorisation. Both scenarios naturally have multiple potential solutions. We were told, however, that the simplest solution, namely locking the machine with a key, cannot be realistically used, as employees usually leave the keys in the machine for practical reasons.

  • Inspection of personal protective equipment 

– safety helmets, vests, facemasks, etc.; in this case, the machine can only be started by a worker who is properly equipped, but we were asked for something much simpler: they requested that the machine check whether night shift workers are wearing protective equipment (when the managers aren’t present) and record violations.

Worker safety 

  • the system can distinguish between the human body and processed materials
  • the software can recognize individuals and allow (or not allow) them to operate a machine
  • safety helmets, vests, facemasks, etc. – the machine can only be started by a worker who is properly equipped


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