We are a young and dynamic team led by senior developers with many years of experience in developing video management products for video processing. We have utilized our knowledge to create CAMAN, a modern and powerful deep learning analytical solution.
In addition to our own development team, we actively collaborate with the academic community and engage university students in development programs focused on image data processing, analytical solutions, and particularly in artificial intelligence projects based on machine learning and neural networks.
Our products are designed for practical, real-life applications, aiming to maximize the safety of people in urban areas and transportation. These goals are achieved through two product lines. Highway-CAMAN is a solution specifically designed for detecting incidents on highways and expressways. It enhances the safety of road users and provides valuable statistical data to road administrators, as well as a straightforward representation of metadata, thanks to integration with C-ITS, SCADA, and GENETEC VMS platforms.
City-CAMAN, on the other hand, analyzes events within Smart Cities, significantly increasing the safety of individuals in traffic and urban environments. It not only improves the quality of life for city residents but also simplifies the work of road managers and municipal technical services.
In conjunction with the D4 highway bypass and R7 expressway project implementation, we deployed the HighwayCAMAN solution to analyze and detect traffic incidents effectively. As part of this project, a deep learning analytical solution was implemented across 76 sections, identifying various traffic incidents such as vehicle stops, standstills, turns, wrong-way driving, traffic congestion, cyclists on the road, pedestrians, and animals on the roadway.
The solution achieves a reliability of 96 to 98%, accounting for varying weather conditions, and surpasses the client’s expectations for event evaluation accuracy. During practical deployment, traffic incident detection saw a 63% increase compared to previous methods.
The primary goal of this AIDL project was to enhance the safety of road users in the city of Svätý Jur. The chosen locations for analysis were particularly significant due to their complexity, considering the feasibility of deploying standard safety measures, such as traffic signs.
Within the scope of the project, metadata from critical traffic incidents are gathered to analyze and identify suitable tools to improve road user safety.
Implementation of a mobile solution for municipal waste collection vehicles aimed at analyzing potholes in the city’s infrastructure and evaluating the visibility of traffic signs. This is integrated with a centralized software solution for seamless coordination.
In conjunction with the D4 highway bypass and R7 expressway project implementation, we deployed the HighwayCAMAN solution to analyze and detect traffic incidents effectively. As part of this project, a deep learning analytical solution was implemented across 76 sections, identifying various traffic incidents such as vehicle stops, standstills, turns, wrong-way driving, traffic congestion, cyclists on the road, pedestrians, and animals on the roadway.
The solution achieves a reliability of 96 to 98%, accounting for varying weather conditions, and surpasses the client’s expectations for event evaluation accuracy. During practical deployment, traffic incident detection saw a 63% increase compared to previous methods.
The primary goal of this AIDL project was to enhance the safety of road users in the city of Svätý Jur. The chosen locations for analysis were particularly significant due to their complexity, considering the feasibility of deploying standard safety measures, such as traffic signs.
Within the scope of the project, metadata from critical traffic incidents are gathered to analyze and identify suitable tools to improve road user safety.
Implementation of a mobile solution for municipal waste collection vehicles aimed at analyzing potholes in the city’s infrastructure and evaluating the visibility of traffic signs. This is integrated with a centralized software solution for seamless coordination.
Máchova 6, 821 06 Bratislava
Chvalova 12, 130 00 Praha 3
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