Embien helps product teams develop video analytics on embedded systems that detect, track, and act on what a camera sees — without shipping every frame to the cloud. From edge AI video analytics pipelines to object tracking with image stitching across multiple camera feeds, our engineers deliver video analytics on embedded systems that run in real time on the device itself.
Plenty of teams can run a video analytics demo on a laptop with a GPU. Far fewer can develop video analytics on embedded systems that hit real-time frame rates on a power- and thermally-constrained device, in the field, without a cloud connection to lean on.
That's the engineering gap Embien fills. Real time video processing on embedded devices means pipelining camera capture, frame pre-processing, model inference, and post-processing logic so the whole chain runs inside the device's power and latency budget — not just the model. Our edge AI video analytics work has included multi-camera object tracking with image stitching, where feeds from several cameras have to be aligned and tracked as one continuous scene rather than several disconnected clips. Explore the broader engineering scope on our edge video analytics hub.
Four things product teams evaluate before choosing a partner to develop video analytics on embedded systems.
Camera-to-decision pipelines engineered for real time video processing on embedded devices, tuned to hit frame-rate targets, not just accuracy benchmarks.
Object tracking with image stitching across multiple camera feeds, so a scene stays coherent as an object moves between fields of view.
Model porting and optimization for edge AI video analytics across Jetson, ARM SoC, and FPGA-accelerated hardware.
The same team that develops video analytics on embedded systems also owns the camera hardware and firmware layer beneath it.
Frame-rate-accurate analytics pipelines running on-device.
Detection, classification, and tracking models tuned for embedded hardware.
Camera and pipeline engineering for demanding frame-rate requirements.
On-device processing that keeps latency and bandwidth under control.
Claims about edge AI video analytics are easy to make. Here's a real one: our team built an object tracking with image stitching system that fuses multiple camera feeds into a single tracked scene, running as real time video processing on embedded devices rather than as an offline batch job. It's the same engineering discipline behind every project where we develop video analytics on embedded systems, whatever the camera count or target hardware.
It means fitting the entire capture-to-decision pipeline inside the device's power, thermal, and latency budget — camera integration, model optimization for the target silicon, and real time video processing on embedded devices, not just a model that runs well on a workstation GPU.
Tell us about your cameras, target hardware, and the decision the system needs to make. An engineer will follow up.