Edge AI in video cameras: how OpenIPC, an open project, is changing the rules of video analytics

Imagine a surveillance camera that doesn't just record video, but independently recognizes objects, analyzes behavior, and makes decisions — without a single call to a cloud server. Just recently, this sounded like science fiction, but the OpenIPC community is already turning this idea into working technology accessible to everyone.

At the AI Conf 2025 applied conference, reverse engineer Dmitry Ilyin presented the second part of a large-scale presentation about the OpenIPC project — an open Linux distribution that was originally created for flashing conventional IP cameras, and over time grew into a full-fledged ecosystem for Edge AI. The abbreviation IPC stands simply for IP Camera, but behind this simplicity lies an ambitious task: to transfer computational intelligence from distant data centers directly to the end device.

To understand the significance of this approach, it's worth recalling how traditional video analytics works. A camera captures a video stream and sends it to a server — local or cloud — where a neural network processes the image and returns the result. This scheme requires stable broadband connectivity, introduces delays, and costs money for each gigabyte of traffic. Edge AI flips this architecture: all processing happens directly on the camera's chip. No internet dependency, minimal latency, complete data control. In the first part of his presentation, Ilyin detailed a comparison of both approaches and discussed alternatives to classical GPUs — specialized neural processors built into modern camera chipsets for surveillance.

The second part focused on tasks that go beyond a single camera. Multi-camera arrays are systems where dozens and hundreds of devices work in coordination, exchanging metadata instead of raw video. Here Edge AI reveals its main trump card: when each camera preprocesses the image and transmits only the analysis results, the network load drops by orders of magnitude. For enterprises, logistics centers, and urban infrastructure, this means a dramatic reduction in costs for communication channels and server equipment.

The topic of multisensor cameras deserves special attention. Modern devices increasingly combine an optical sensor with infrared, thermal, or even lidar modules. When data from multiple sensors are combined and processed directly on the device, recognition quality increases significantly. A person can be identified in complete darkness, a live operator distinguished from a dummy, an object's temperature determined without additional equipment. It is precisely the fusion of sensor data at the Edge level that makes such scenarios practically feasible without expensive server infrastructure.

The OpenIPC project is notable not only for its technology but also for its development model. It's a classic open-source community where each participant brings unique expertise: someone understands the architecture of a specific chipset, someone optimizes neural network models for limited resources, someone writes drivers. This distributed knowledge model allows the project to support dozens of camera models from different manufacturers and quickly adapt to new hardware. In fact, OpenIPC does for video cameras what OpenWrt once did for routers — it frees the device from the constraints of proprietary firmware and opens up space for experimentation.

The trend of moving artificial intelligence closer to end devices is picking up steam far beyond video surveillance. Major chip manufacturers — from HiSilicon to Ambarella and Ingenic — are ramping up neural computing blocks in their processors, and the Edge AI market, according to analyst forecasts, will exceed 50 billion dollars by 2028. But while corporations build closed ecosystems, projects like OpenIPC demonstrate that powerful video analytics can be open, flexible, and accessible.

Dmitry Ilyin's presentation clearly demonstrates an important shift in the industry: the future of video analytics lies not in giant cloud clusters, but in smart devices at the network's edge. And the more powerful embedded neural processors become, the less reason there is to send video streams far away for processing. A camera that understands what it sees on its own is no longer a concept, but a working reality.