Multi-Camera Streaming & DeepStream Redesign
Redesign of a production video-AI stack for continuous multi-stream processing and GPU inference
ITarget operates a multi-stream video platform that combines live streaming, computer vision and machine learning.
The existing architecture could not hold stable, continuous operation. Streaming, GPU inference and the ML layer had to be redesigned together rather than patched separately.
- The platform runs 24/7 - the redesign had to target long-running deployment, not single-session performance.
- Multiple concurrent streams share the same GPU budget; per-stream efficiency directly determines how many cameras the platform can serve.
- Latency between a live frame and its inference output had to stay inside a real-time window.
- Existing camera infrastructure and customer integrations had to keep working through the transition.
The redesigned stack moves all video handling onto the GPU with DeepStream at the center:
Streams are decoded and batched once, inference runs on GPU-resident frames, and the ML pipeline consumes structured results instead of raw video.
- DeepStream pipeline redesign: batched decoding, zero-copy GPU transport and a restructured inference graph.
- Streaming optimisation for stable long-running sessions, with backlog growth and resource pressure on the hot path addressed.
- GPU inference restructuring so concurrent streams share the device predictably.
- Machine learning architecture rework so downstream models consume the stream of detections, not the video itself.
The redesigned pipeline sustains continuous 24/7 operation with the live pipeline running at 30 FPS.
The platform now runs as infrastructure: long-running multi-camera deployments that keep operating without daily intervention.
Selected implementation details are withheld under confidentiality obligations.
Running into a similar constraint in your own pipeline? Bring the stream specs and we will scope the audit.
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