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Camerite cuts development time 25.2% with Ultralytics YOLO

Learn how Camerite uses Ultralytics YOLO to automate video monitoring, cutting development time and helping cities shift to proactive security.

Camerite cuts development time 25.2% with Ultralytics YOLO

Problem

Traditional video monitoring depends on human operators watching multiple screens at once, which limits how many relevant events can be caught, increases the risk of missed incidents, and makes it hard to scale operations across thousands of cameras.

Solution

Camerite's AI-powered platform uses Ultralytics YOLO models to analyze real-time video streams, automatically detecting people, vehicles, and other objects of interest, generating instant alerts, and turning raw footage into structured, actionable data.

Camerite is a video monitoring platform based in Brazil that transforms cameras of any brand and model into intelligence and security resources. Its platform is completely equipment-agnostic, meaning the value it delivers lives in the software layer rather than the hardware, so any existing camera or DVR can be turned into a smart monitoring point. By applying computer vision technology to video streams, Camerite helps make cities smarter and safer by helping companies, residential communities, industrial facilities, and public entities extract more value from the footage they already generate.

From passive recordings to active intelligence#

The video monitoring industry generates an overwhelming volume of imagery every day, but historically that footage has offered little real capacity for analysis or prevention. Most systems still depend on operators manually watching multiple screens simultaneously to catch relevant events, an approach that limits scalability, increases the risk of missed incidents, and makes consistent, real-time response difficult across large camera networks.

Camerite was built on the idea that this gap between "recording" and "understanding" could be closed with computer vision models like Ultralytics YOLO. As AI and computer vision matured, it became possible to turn ordinary cameras into smart sensors capable of detecting objects, identifying events, generating alerts, and supporting decisions in real time, rather than simply archiving hours of footage that no one has time to review.

Automating detection with Ultralytics YOLO#

To close that gap, Camerite built its platform powered by Ultralytics YOLO models as the core object detection component. The models are trained to analyze live video streams and automatically identify people, vehicles, and other elements of interest, feeding that information into automatic alert generation systems instead of relying on continuous human observation.

The platform is designed to scale to thousands of cameras running simultaneously, and its detection capabilities are applied across several types of deployments:

  • Security and surveillance: threat detection, perimeter intrusion detection, identification of suspicious movements, monitoring of restricted areas, and real-time alert generation.
  • Urban monitoring: vehicle flow control, support for municipal monitoring centers, and supervision of public spaces.
  • Business operations: access control, facility monitoring, and automatic detection of operational events.

Camerite vehicle counting (1) Fig. 1 Caramite’s platform real-time video detection for urban monitoring.

Because the platform works with any camera brand or model, Camerite can bring the same YOLO-powered detection to a wide range of environments, from residential complexes to industrial sites to city-wide monitoring centers, without requiring clients to replace their existing hardware.

Evaluating models against real production requirements#

Before settling on a detection approach for a given deployment, Camerite's team evaluates candidate models against a consistent set of production-oriented metrics, including precision, recall, mAP, false positive and false negative rates, inference time, FPS, computational consumption, and real-world performance. Each model is audited on total objects detected, true positives, false positives, false negatives, and correct classification, while comparative tests across scenarios are used to weigh overall accuracy against total processing time and real-time performance.

That evaluation process, combined with the flexibility to train and adapt models to different business scenarios, is what allows Camerite to deploy the same underlying detection technology across such varied use cases, from perimeter intrusion in an industrial facility to vehicle counting in a municipal monitoring center.

Turning video into a strategic data source#

By automating detection with Ultralytics YOLO, Camerite converts video from a passive archive into a structured, real-time data source. Instead of relying exclusively on human observation, clients get automated analytics that continuously process large volumes of images and surface only the events that matter, which the team reports has translated into faster response times, greater operational scalability, less repetitive manual work, and better use of monitoring teams. Adopting YOLO also helped Camerite reduce development time for each new service by 7.5%, since the same detection framework can be adapted and retrained for new scenarios rather than built from scratch each time.

The impact extends beyond internal operations. Camerite's clients, including public safety partners, have used the platform's detections to support law enforcement with the seizure of more than 10 tons of narcotics, the recovery of more than 450 vehicles, electronics, and other items, and have contributed to more than $30 million (BRL) in financial losses inflicted on criminal operations. Clients have also reported increased operational visibility, reduced incident response times, stronger monitoring team efficiency, and greater confidence in the alerts the platform generates.

Building Brazil’s largest image-based intelligence network#

Camerite's long-term vision is to become the largest generator of image-based information in monitored environments in Brazil, and Ultralytics YOLO is central to getting there. By combining computer vision with artificial intelligence, Camerite is helping clients move from a reactive monitoring model, where footage is only reviewed after something goes wrong, to a preventive, data-driven approach where relevant events are caught and acted on as they happen.

As the platform continues to scale across new industries and camera networks, Camerite's equipment-agnostic, YOLO-powered approach positions it to keep expanding what's possible when thousands of ordinary cameras are turned into an intelligent, real-time security network.

Interested in building Vision AI solutions of your own? Explore Ultralytics YOLO models, learn how YOLO is driving innovation across industries, and check out our licensing options to get started.

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Frequently asked questions

  • Ultralytics YOLO repositories are distributed under the AGPL-3.0 License by default. This OSI-approved license is designed for students, researchers, and enthusiasts, promoting open collaboration and requiring that any software using AGPL-3.0 components also be open-sourced. While this ensures transparency and fosters innovation, it may not align with commercial use cases.

    If your project involves embedding Ultralytics software and AI models into commercial products or services and you wish to bypass the open-source requirements of AGPL-3.0, an Enterprise License is ideal.

    Benefits of the Enterprise License include:

    • Commercial flexibility: Modify and embed Ultralytics YOLO source code and models into proprietary products without adhering to the AGPL-3.0 requirement to open-source your project.
    • Proprietary development: Gain full freedom to develop and distribute commercial applications that include Ultralytics YOLO code and models.

    To ensure seamless integration and avoid AGPL-3.0 constraints, request an Ultralytics Enterprise License using the form provided. Our team will assist you in tailoring the license to your specific needs.

  • The model you choose depends on your project requirements, including performance, accuracy, deployment target, and hardware constraints. For most new projects, Ultralytics YOLO26 is the recommended starting point because it offers the latest improvements in speed, accuracy, exportability, and multi-task support.

    Earlier YOLO model families remain available for teams with existing workflows or compatibility requirements.

    If you are starting fresh, choose YOLO26 first, then benchmark smaller or larger variants to find the right balance of speed and accuracy for your deployment environment.

  • Ultralytics YOLO models are a family of computer vision models for tasks such as object detection, segmentation, classification, pose estimation, and oriented object detection. YOLO26 is the latest stable version and is recommended for most new projects. Earlier YOLO versions remain available for teams with existing workflows or compatibility requirements.

  • Ultralytics YOLO models are computer vision architectures developed to analyze visual data from images and video. These models can be trained for tasks including object detection, classification, pose estimation, tracking, instance segmentation, and oriented object detection.

    The latest Ultralytics YOLO model family is YOLO26, with earlier YOLO versions available for existing workflows.

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