Understanding the Two Approaches: Mainstream and Substream
In-store video analysis relies on data streams captured by cameras installed in the retail environment. These streams can be processed using two approaches: *mainstream* and *substream*. The mainstream is the primary video stream, usually in high definition, which offers an optimal level of detail for advanced analytics. The substream, on the other hand, is a lighter, secondary stream, often compressed to reduce processing load and limit bandwidth usage.
While the substream may seem appealing at first due to its low technical requirements, it quickly shows its limitations in demanding use cases, particularly for the precise detection of suspicious gestures or behaviours. Subtle details – movements, interactions – are often lost in a low-resolution stream. This significantly undermines the accuracy of AI models that require rich, well-defined data to deliver reliable results.
In comparison, mainstream analysis relies on high-fidelity images. This allows for a more detailed interpretation of gestures and postures, which is essential for retail stores, where the risks of shrinkage are high and often linked to subtle behaviours.
The Operational Advantages of Mainstream Analysis 📈
One of the major advantages of mainstream processing is its ability to feed artificial intelligence algorithms with high-quality, precise, and consistent data. This translates into more reliable and actionable results for security teams and store managers. The detection of suspicious gestures – concealing items, inconsistent movements, abnormal interactions – becomes more relevant, reducing false positives and improving on-the-ground response times.
Unlike the substream, which can produce blurry, pixelated, or compressed images, the mainstream ensures a clear visual foundation for the AI to build upon. This also allows for better event traceability, which is useful for internal investigations or post-incident analyses.
For decision-makers, this means a tangible reduction in losses, a better customer experience – as interventions are more targeted and less intrusive – and enhanced surveillance system effectiveness, without needing to increase the number of cameras or modify existing infrastructure.
Why the Substream Falls Short for Next-Generation AI
As AI rapidly advances and becomes capable of recognising complex gestures and human interactions, relying on the substream becomes a major technical limitation. Its low bitrate compromises the quality of the collected data, making certain analyses impossible or inaccurate.
Furthermore, in a context where threats are becoming more subtle, identifying obvious behaviours is no longer enough. It is often the subtle gestures, the unusual movements, that allow for the detection of a real risk. These nuances disappear in substream feeds, thereby reducing the foresight and responsiveness of security teams.
Choosing a substream-based solution, therefore, means risking an investment with limited returns. Conversely, the mainstream becomes a strategic foundation for stores that leverage technology to improve both their security and their business performance.
Oxania, Your Technology Partner for High-Quality Video Analysis 🚀
At Oxania , we have made the clear choice to harness the full potential of the mainstream feed. Our artificial intelligence video analysis solutions are designed to operate on high-definition streams, ensuring maximum reliability in detecting suspicious gestures and seamless integration into video surveillance systems. Thanks to this approach, our clients benefit from a better return on investment and a enhanced level of security .
By choosing mainstream analysis, you provide your teams with more precise tools and more relevant alerts. It is a powerful lever for combining operational efficiency with technological innovation.
Adopting mainstream analysis means entering a new era of intelligent surveillance, where data quality makes all the difference.
