Mainstream and substream: understanding the two video streams
Video analytics in stores relies on data streams captured by cameras installed in the retail environment. These streams can be processed using two approaches: mainstream and substream. Mainstream corresponds to the primary video stream, generally in high definition (1080p, 4K or higher), which offers an optimal level of detail for advanced analytics. Substream, on the other hand, is a lighter secondary stream, often compressed to reduce processing load and limit bandwidth usage.
While substream may seem attractive at first glance due to its low technical requirements, it quickly shows limitations in demanding use cases, particularly the precise detection of suspicious gestures or abnormal behaviors. Subtle details, hand movements, interactions with products, are often lost in a low-resolution stream. This significantly harms the accuracy of AI models, which require rich and well-defined data to deliver reliable results.
In comparison, mainstream analysis relies on high-fidelity images. This enables a more precise reading of gestures and postures, which is essential for retail businesses, where the risks of shrinkage are high and often linked to subtle behaviors. According to the 2025 NRF (National Retail Federation) report, shoplifting incidents increased by 18% year-over-year, making detection accuracy more critical than ever.
The operational benefits of high-resolution video analytics 📈
One of the major advantages of mainstream processing lies in its ability to feed artificial intelligence algorithms with high-quality, accurate, and consistent data. This translates into more reliable and actionable results for security teams and store managers. Detecting suspicious gestures, concealing objects, inconsistent movements, and abnormal interactions becomes more relevant, reducing false positives and improving response time on the ground.
Unlike substream, which can produce blurry, pixelated, or compressed images, mainstream ensures a clear visual foundation that AI can rely on. Some solutions, such as Oxania's, leverage dedicated GPUs to process the mainstream feed in real time, combining high resolution and low latency, a major difference from solutions limited to substream due to computing power constraints. This also enables better event traceability, useful for internal investigations or post-incident analysis.
For decision-makers, this means a concrete reduction in losses, an improved customer experience, since interventions are better targeted and less intrusive, and enhanced efficiency of surveillance systems, without needing to increase the number of cameras or modify existing infrastructure. The choice of equipment also plays a decisive role: discover how to choose the right video surveillance camera for your store.
Why substream is no longer enough for AI detection
As AI advances rapidly and becomes capable of recognizing complex gestures and human interactions, reliance on substream becomes a major technical limitation. Its low bitrate compromises the quality of collected data, making certain analyses impossible or inaccurate. The AI video analytics market is experiencing sustained growth, with a compound annual growth rate (CAGR) of nearly 23% expected by 2031, a sign that image quality requirements continue to rise.
Moreover, in a context where threats are becoming more subtle, it is no longer enough to identify obvious behaviors. It is often unusual gestures, micro-movements, that reveal a real risk. These nuances disappear in substream feeds, reducing the ability of security teams to anticipate and respond. As highlighted by the ECR Retail Loss community, which brings together more than 400 global retailers, shrinkage represents a challenge worth tens of billions of dollars worldwide.
Choosing a substream-based solution therefore means taking the risk of a limited return on investment. Conversely, mainstream becomes a strategic foundation for stores betting on technology to improve both their security and their business performance.
Oxania: mainstream video analytics for integrators 🚀
At Oxania, choosing the mainstream feed is a deliberate technological commitment. Our AI-powered video analytics solutions leverage high-definition streams processed by dedicated GPUs, ensuring maximum reliability in detecting suspicious gestures and seamless integration into existing video surveillance systems. This architecture, designed for video surveillance and loss prevention integrators, ensures a professional deployment tailored to field constraints.
By choosing mainstream analytics, you provide your teams with more accurate tools and more relevant alerts. It's a powerful lever for combining operational efficiency with technological innovation.
Adopting mainstream analytics means entering a new era of intelligent surveillance, where data quality makes all the difference.
