Combine high-quality video monitoring, AI-powered detection, and innovative technology to deliver the ultimate protection for your customers.
Fluendo’s expertise spans video capture, analysis, transmission, and secure storage for later review, making us a trusted technological partner for improving and developing advanced video surveillance solutions. With deep knowledge of major multimedia frameworks, we will help you boost your solution with the latest state-of-the-art technologies.
Fluendo’s expertise spans video capture, analysis, transmission, and secure storage for later review, making us a trusted technological partner for improving and developing advanced video surveillance solutions. With deep knowledge of major multimedia frameworks, we will help you boost your solution with the latest state-of-the-art technologies.
No matter what operating system or multimedia framework you are using, Fluendo expertise can reduce your time-to-market.
Rely on production ready components that provide stability to your applications, improving your capturing and streaming processes. No more hard-resets to your service.
Leverage multimedia edge AI technology to improve your detection system.
Improve your reliability by having proper validations systems that can emulate your application as a whole with long running and regression tests.
Bring latest codecs into your newly created media files to reduce your storage size.
No matter what operating system or multimedia framework you are using, Fluendo expertise can reduce your time-to-market.
Rely on production ready components that provide stability to your applications, improving your capturing and streaming processes. No more hard-resets to your service.
Leverage multimedia edge AI technology to improve your detection system.
Improve your reliability by having proper validations systems that can emulate your application as a whole with long running and regression tests.
Bring latest codecs into your newly created media files to reduce your storage size.
The client was transitioning from a legacy Windows-only application to a modern, cross-platform solution using GStreamer, targeting Windows, macOS, and Linux. The application, built with a C# front end and a C/C++ backend layer, suffered from instability, including memory leaks and frequent crashes.
We were brought in mid-development to help bring the project back on track. Our involvement focused initially on stabilizing the system, followed by the implementation of new features such as masking and video wrapping. Leveraging our GStreamer expertise, we explored and tested multiple architectural approaches, ultimately delivering a stable and functional application in time for the product’s launch.
We provided GStreamer expertise to support the development of a native Windows application built on the GStreamer framework. This included a major codebase refactor to resolve critical bugs, enhance performance, and improve overall system stability. To ensure long-term success, we also delivered tailored GStreamer training to the client’s team, empowering them to maintain and evolve the application independently.
For a deeper look at how we transformed a Windows-only legacy app into a modern, cross-platform GStreamer solution, check out our article Revolutionizing video management: GStreamer project on Windows, where we share insights that echo the work delivered in this project.
Video surveillance
From unstable legacy app to a stable, cross-platform GStreamer solution
Video surveillance
From unstable legacy app to a stable, cross-platform GStreamer solution
The client, a global provider of hardware and software for video surveillance systems, leverages AI to analyze video streams from multiple IP cameras. These streams are then aggregated, encoded, and delivered in real time through their client-facing web application.
As they transitioned from an FFmpeg-based solution to GStreamer, the client engaged us to perform an in-depth analysis of their new streaming pipelines. Leveraging our GStreamer expertise, we identified key areas for optimization and enhanced their pipelines to take full advantage of NVIDIA® hardware acceleration for improved performance and efficiency.
We conducted a thorough analysis of the client’s GStreamer pipelines and underlying source code. Based on our findings, we proposed a revised architecture designed to enable zero-copy processing, leverage NVIDIA® hardware acceleration, and ensure seamless integration with the WebRTC streaming protocol.
Video surveillance
GStreamer, codecs (audio and video), ffmpeg
Optimized real-time surveillance streaming with WebRTC and zero-copy processing
Video surveillance
GStreamer, codecs (audio and video), ffmpeg
Optimized real-time surveillance streaming with WebRTC and zero-copy processing
The client needed a robust video management system (VMS) application to support real-time security monitoring and playback of surveillance footage from various locations—on-site, centralized, and mobile. The system architecture consisted of a C#-based graphical user interface (GUI) and a C/C++ multimedia backend, with a clear separation between the two components.
To support maintainability and catch issues early in the development cycle, the client required a reliable testing strategy for the backend. Rather than traditional unit tests—which were not suitable for this phase—we proposed and implemented a suite of functional automated tests using the backend’s JSON API. These tests were designed to integrate with the client’s CI/CD pipeline, enabling early detection of bugs and improving development efficiency.
We established a streamlined development workflow focused on quality and reliability. As part of the solution, we integrated automated functional testing into the continuous integration process. This ensured consistent validation of the multimedia backend and enabled the generation of clear, comprehensive reports. These reports made it easy for the client to track progress, identify issues early, and maintain confidence in the stability of the system throughout development.
If you want to learn more about how automated testing strengthens multimedia applications, take a look at our article Integrating automated testing in multimedia, where we share best practices and insights that complement the work done in this project.
Early bug detection and greater system stability
Early bug detection and greater system stability
The surveillance industry secures public spaces, infrastructure, transport, and private property, but high-resolution, continuous monitoring raises privacy concerns and legal challenges under regulations like GDPR (General Data Protection Regulation). To meet these demands, organizations need real-time video anonymization that obscures identifiable features (e.g., faces) while maintaining video utility for security and analytics.
AI and computer vision now make it possible to anonymize individuals either at the edge or during post-processing, supporting legal and ethical surveillance practices.
Anonymizer is a Fluendo AI Plugin which integrates in existing CCTV or IP camera networks at the edge (on-site devices or local servers) or in a centralized processing unit via GStreamer. This component automatically detects and anonymizes all faces in the video stream without human intervention.
In a centralized setup, anonymized video is streamed in real time for monitoring, while raw footage is securely stored locally. This dual-mode approach ensures privacy compliance during daily operations while preserving full-resolution evidence for authorized legal or audit use.
Additionally, the system can act as a real-time middleware, offering three simultaneous modes: streaming anonymized video, streaming non-anonymized video, and storing both versions in parallel. This flexible architecture adapts to diverse operational and regulatory needs.
Supports GDPR, and similar laws by anonymizing identifiable data before storage or analysis.
Instantly anonymize scenes without manual editing, saving your team from the burden of blurring or cutting footage before sharing.
All footage is processed and stored locally, keeping sensitive content under your control. No need to send data to the cloud for anonymization — ensuring compliance and minimizing external exposure.
Supports GDPR, and similar laws by anonymizing identifiable data before storage or analysis.
Instantly anonymize scenes without manual editing, saving your team from the burden of blurring or cutting footage before sharing.
All footage is processed and stored locally, keeping sensitive content under your control. No need to send data to the cloud for anonymization — ensuring compliance and minimizing external exposure.
In high-risk environments such as elderly care facilities, hospitals, public transit stations, or workplace safety zones, detecting human falls instantly is essential for preventing serious injuries and enabling rapid emergency response. However, continuous manual video surveillance monitoring is resource-intensive and prone to oversight.
A growing need exists for automated fall detection systems that operate in real-time, are accurate, and respect user privacy. Such a system would assist caregivers, security teams, or emergency responders by automatically identifying falls and triggering alerts, without relying on wearable sensors.
With advancements in AI pose estimation systems, a computer vision–driven solution could be deployed directly on-site to provide privacy-compliant, accurate, and low-latency fall detection.
Our fall detection application uses a deep learning model to estimate body key points and track motion trajectories frame-by-frame. By analizing changes in body angles, joint angles and movement velocity, the system can accurately detect genuine falls while filtering out benign movements (e.g., sitting or bending). This fall detection solution is designed for seamless integration and works with standard CCTV and IP camera streams using a GStreamer pipeline.
Enables immediate response to incidents, reducing injury severity and improving care quality.
Easy plug-and-play integration with existing systems, combined with configurability, enabling quick deployment and customization to meet diverse environment requirements.
Processes data locally, without needing personal identity recognition—ideal for GDPR-sensitive environments.
Enables immediate response to incidents, reducing injury severity and improving care quality.
Easy plug-and-play integration with existing systems, combined with configurability, enabling quick deployment and customization to meet diverse environment requirements.
Processes data locally, without needing personal identity recognition—ideal for GDPR-sensitive environments.
Public spaces such as transportation hubs, university campuses, shopping centres, and event venues face growing security risks from both concealed and openly carried knives. These threats put heavy pressure on security teams to detect weapons quickly and respond before incidents escalate. Traditional manual monitoring of multiple CCTV or IP camera feeds is labour-intensive and prone to human error and delayed reactions, particularly in crowded or fast-moving environments.
An AI-powered, real-time knife detection system can significantly improve situational awareness by automatically scanning live video streams for potential weapons and sending instant alerts to security personnel. Thanks to recent advances in computer vision and low-latency edge computing, these intelligent detection models can now be seamlessly integrated into existing surveillance networks without costly infrastructure changes.
Our advanced deep learning object detection models are trained on diverse datasets that cover varying lighting conditions, camera angles, and crowd densities. This enables the system to recognize knives accurately in live and recorded video streams, even in challenging environments.
Integrated into the GStreamer multimedia framework, the AI pipeline continuously processes incoming CCTV or IP camera feeds in real time, delivering high detection accuracy with low false-positive rates. The result is a reliable, scalable, and proactive security tool that enhances public safety by identifying potential threats as they happen.
Identifies potential threats early, allowing security teams to intervene before incidents escalate. This proactive approach reduces risks by enabling swift response in high-traffic, dynamic environments, enhancing overall safety.
Trained on diverse scenarios, our AI-driven system minimizes false positives while maintaining high sensitivity. It ensures accurate, reliable threat detection across varying conditions, providing security teams with precise alerts in dynamic environments.
Processes data locally for faster performance and enhanced data security. This approach ensures quick response times while maintaining privacy and compliance, making it ideal for real-time threat detection in dynamic and sensitive environments.
Identifies potential threats early, allowing security teams to intervene before incidents escalate. This proactive approach reduces risks by enabling swift response in high-traffic, dynamic environments, enhancing overall safety.
Trained on diverse scenarios, our AI-driven system minimizes false positives while maintaining high sensitivity. It ensures accurate, reliable threat detection across varying conditions, providing security teams with precise alerts in dynamic environments.
Processes data locally for faster performance and enhanced data security. This approach ensures quick response times while maintaining privacy and compliance, making it ideal for real-time threat detection in dynamic and sensitive environments.
In modern live broadcasting and video surveillance, operators struggle to keep moving subjects framed when using traditional PTZ (Pan-Tilt-Zoom) cameras in dynamic environments such as lecture halls, theaters, or public spaces. An AI-driven subject tracking solution can autonomously detect and follow speakers, performers, or persons of interest, reducing reliance on manual control and ensuring consistent coverage.
By integrating real-time subject tracking with PTZ hardware, this technology maintains consistent coverage even when subjects move unpredictably. Recent breakthroughs in low-latency edge computing make it possible to deploy this system on-site for both live event production and security surveillance, delivering precise and reliable tracking without additional operators.
Recent low-latency edge computing advancements make precise subject tracking feasible for both live production and surveillance.
Our smart PTZ camera control solution combines real-time multi-target tracking with precise pan, tilt, and zoom control to keep your subjects in view at all times. Integrated via a GStreamer pipeline and deployed directly on-site, the system processes live video feeds to detect and assign bounding boxes to selected targets.
A bidirectional PTZ interface translates bounding box movement into smooth pan, tilt, and zoom commands, ensuring seamless tracking without abrupt or jerky transitions. Equipped with re-identification capabilities, the tracking module can handle occlusion and rapid movement and re-acquire targets even after they leave and re-enter the frame.
Minimizes manual intervention and streamlines operations, enabling more efficient workflows and reducing the need for dedicated resources in live production or surveillance.
Delivers precise, professional-level shot composition with high-quality tracking of moving subjects, ensuring optimal visual clarity in live events and surveillance.
Enables efficient, on-site tracking with minimal infrastructure, offering customizable solutions for mobile rigs and remote setups.
Minimizes manual intervention and streamlines operations, enabling more efficient workflows and reducing the need for dedicated resources in live production or surveillance.
Delivers precise, professional-level shot composition with high-quality tracking of moving subjects, ensuring optimal visual clarity in live events and surveillance.
Enables efficient, on-site tracking with minimal infrastructure, offering customizable solutions for mobile rigs and remote setups.
In retail, logistics, and critical infrastructure security sectors, detecting suspicious or abnormal behavior quickly is essential for preventing losses and mitigating risks A common risk scenario is when individuals loiter or linger in restricted or high-value areas (e.g., product aisles, exits, stockrooms, or sensitive perimeters) beyond normal dwell times, behavior that may indicate theft, intrusion, or a developing security threat. Traditional methods rely on manual CCTV monitoring or basic motion alerts, which are often labor-intensive, prone to false positives, and slow to respond.
AI-powered computer vision now enables a more robust approach by combining person detection, tracking, and region-of-interest (ROI) analysis to identify individuals who remain in defined zones longer than expected—automatically flagging potential threats or anomalies in real time.
The solution leverages a GStreamer-based video pipeline integrated with AI models for person detection and multi-object tracking (MOT). Security teams can define Regions of Interest (ROIs) within each camera view, such as entrances, exits, restricted zones, or high-value product aisles.
The system continuously calculates dwell time within each ROI for every detected individual. When time exceeds a configurable threshold, the system automatically triggers an alert or logs the event for review, enabling early detection of loitering, intrusion, or potential marauding behaviors.
Designed for real-time edge deployment, the system minimizes latency. It reduces bandwidth requirements while supporting centralized deployments for large-scale environments such as retail chains, logistics hubs, and critical infrastructure facilities.
Proactively detects behavior patterns associated with theft, loitering, or tampering before incidents occur.
By lowering dependence on manual surveillance, organizations can reduce operational expenses.
Enhances the perception of safety and control in stores, warehouses, or public venues—reinforcing customer and employee confidence.
Proactively detects behavior patterns associated with theft, loitering, or tampering before incidents occur.
By lowering dependence on manual surveillance, organizations can reduce operational expenses.
Enhances the perception of safety and control in stores, warehouses, or public venues—reinforcing customer and employee confidence.
Fluendo’s expertise in video streaming and rendering and collaborative approach to development has allowed us to deliver more value to our customers in a shorter time. After the product launch, Fluendo has continued providing timely support to ensure our customers receive a best-in-class video experience.
Our way to ultra-fast AI video processing, Fluendo AI Plugins. Discover more in this article.
Real-time AI and GDPR compliance don’t have to be at odds. Learn how Fluendo’s Raven Engine makes privacy-first, edge-based AI video analytics a reality—without compromising on speed or accuracy.
Our custom AI plugin leverages advanced GAN-based models to upscale images with remarkable detail and clarity. It transforms low-res inputs into high-res outputs for medical imaging, satellite imagery, or entertainment, delivering flexibility, precision, and speed. Discover more in this article!
Picture safeguarding privacy in real-time video with effortless, automated face anonymization. That's precisely what Fluendo's Face Anonymization plugins deliver! Discover more in this article.