Videologic Analytics & Ultralytics YOLO for video surveillance

Videologic Analytics is a Spain-based developer of advanced video analytics solutions that enhance security and surveillance for industrial sites, solar parks, and residential complexes. They deploy AI-powered solutions that integrate with security cameras to monitor perimeters and detect intrusions in real time. 

Facing high costs and slow deployment with previous models, they integrated Ultralytics YOLO models to boost detection accuracy, reduce development costs and time to market, and expand into new areas like retail and business intelligence.

Enabling computer vision for security with AI cameras

Led by experts with over 30 years of experience, Videologic Analytics specializes in integrating AI and computer vision into security cameras for real-time monitoring and automated threat detection. Their solutions safeguard large facilities, renewable energy installations, and residential communities with reliable performance.

They serve renowned clients such as Prosegur, Securitas, Sabico, and over 4,000 certified security companies in Spain. Facing challenges with expensive and time-consuming AI model development and deployment, they adopted Ultralytics YOLO models into their innovative Vision AI solutions. By doing so, they were able to enhance their security applications and also branch out into new verticals.

The need for cost-efficient automated threat detection using AI

Videologic Analytics had previously integrated AI models into the security cameras they offered to their clients. These early models were programmed to detect a limited range of object categories, including generic vehicles, humans, and small animals. While this foundational approach laid the groundwork for advanced security systems, it also presented opportunities for further refinement, particularly in enhancing precision and false positive rates.

Their customers were looking for a more comprehensive solution, one capable of delivering broader and more accurate object detection capabilities across a wider range of objects and scenarios. To cater to these customer needs, Videologic Analytics's research and development team began developing enhanced AI models. 

While developing these models, Videologic Analytics quickly found that the existing approach had some issues, such as high costs and long development times. The company realized it needed a more flexible and efficient approach. This new approach would need to tackle these challenges and better serve its clients' evolving security needs. 

Specifically, they wanted to identify a computer vision model that could enhance the reliability of its Vision AI solutions and boost customer satisfaction. It was also essential that the model remained both cost-effective and adaptable to future needs.

Redefining surveillance with AI-driven anomaly detection

After testing several AI models, Videologic Analytics discovered that Ultralytics YOLO models provided the flexibility and performance they needed. They began with pre-trained YOLO models developed using the COCO dataset, which includes a wide range of common objects. This pre-training offered a strong foundation, as the models could already recognize many basic items, making it easier to adapt them for specific security needs.

For example, Videologic Analytics fine-tuned these pre-trained models using their own proprietary data for applications like monitoring solar parks. 

In this scenario, the models were used for AI-driven anomaly detection, distinguishing between genuine threats - such as unauthorized personnel or vehicles - and harmless elements like small animals or wind-blown debris. This clear differentiation was essential in reducing false alarms and improving overall security performance.

Alongside monitoring solar farms, they also developed both industrial and residential security solutions using YOLO, as well as proof-of-concept modules for computer vision innovations in retail and business intelligence. While they primarily use object detection, they also leverage computer vision tasks supported by YOLO, such as pose estimation and object tracking.

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Why choose Ultralytics YOLO models?

Videologic Analytics chose Ultralytics YOLO models because they required a robust solution capable of supporting numerous camera channels while delivering fast, accurate inference. 

YOLO supports various export formats and integrates seamlessly with frameworks like CUDA, TensorRT, ONNX, and OpenVINO. This flexibility makes it possible for Videologic Analytics to fine-tune models using PyTorch and deploy them efficiently in production. With hardware-specific optimizations, YOLO meets the demanding needs of real-time video analytics better than previous models.

Streamlined model deployment for intelligent video monitoring

Since integrating Ultralytics YOLO models, Videologic Analytics has seen impressive improvements in both performance and efficiency. Their new Vision AI solution has enabled rapid, real-time threat detection across a wide range of installations - from solar parks and industrial sites to residential complexes. 

In fact, Videologic Analytics deploys around 10,000 licenses annually, each corresponding to a dedicated camera channel, with all licenses now upgraded to support Ultralytics YOLO models. The shift to YOLO has led to a significant reduction in false alarms and an overall boost in detection accuracy. As a result, customers enjoy more reliable security systems, and operational costs have been lowered. 

Also, the faster inference speeds and scalability of the Ultralytics YOLO models have shortened the time-to-market for new AI features. This has made it possible for Videologic Analytics to enhance its core security offerings and explore new opportunities in verticals such as retail and business intelligence. Overall, the adoption of Ultralytics YOLO models has driven both immediate operational improvements and long-term growth prospects for the company.

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Smart video analytics for safety and security: The road ahead

Videologic Analytics is actively working on expanding its solution by leveraging Ultralytics YOLO models to go beyond basic intrusion detection. The next steps involve providing richer, more actionable insights through advanced analytics such as behavior analysis, trend tracking, and predictive intelligence. 

These enhancements will help customers optimize security operations and unlock new possibilities in retail and business intelligence, driving continued innovation and growth in real-time video analytics.

Curious how computer vision can reshape your business? Explore our GitHub repository to see how Ultralytics’ AI solutions are transforming innovations like AI in self-driving cars and computer vision in agriculture. Find out more about our YOLO models and licensing options, and start your journey toward smarter, more efficient automation today.