{"id":10356,"date":"2026-09-06T07:20:00","date_gmt":"2026-09-06T05:20:00","guid":{"rendered":"https:\/\/www.lukaswojcik.com\/blog\/?p=10356"},"modified":"2026-09-04T23:51:25","modified_gmt":"2026-09-04T21:51:25","slug":"inference-budget-cameras-frame-rate-against-one-tpu","status":"publish","type":"post","link":"https:\/\/www.lukaswojcik.com\/blog\/en\/cctv\/inference-budget-cameras-frame-rate-against-one-tpu\/","title":{"rendered":"Frigate and Coral TPU: How Many Cameras One Accelerator Can Handle"},"content":{"rendered":"

The question asked before every Frigate installation is how many cameras one Coral can carry, and the answer is always a range because the honest form of the question is different. A TPU does not process cameras. It processes inferences, and it has exactly one thousand milliseconds per second to do it in.<\/p>\n

Everything else follows from that: how many detections a second the setup produces, how long one detection takes on the chosen accelerator, and how much of the second is left over when several cameras are busy at the same time. Three numbers, one multiplication.<\/p>\n

\n\"Three
One second of TPU time. The third bar is not slower – it is a setup where half the frames are queued and then thrown away.<\/figcaption><\/figure>\n

What One Detection Actually Costs<\/h2>\n

Frigate does not send every frame to the detector. A cheap CPU pass finds motion first and cuts out the areas where something changed; only those regions go to the TPU, each as its own inference. A still scene therefore costs nothing at all, and a scene with two separate moving areas costs two inferences per frame rather than one.<\/p>\n

This is the detail that turns camera counts into guesswork. The load is not cameras times frame rate – it is cameras times frame rate times the number of motion regions, and the last factor is a property of the view, not of the hardware. A camera pointing at a hedge in the wind produces regions all day; one pointing at a closed gate produces none for hours.<\/p>\n

The Calculation in One Line<\/h2>\n
inferences\/s = cameras × detect fps × regions per frame\noccupancy    = inferences\/s × inference time\nheadroom     = 1000 ms\/s − occupancy\n\n8 cameras × 5 fps × 1.5 regions = 60 inferences\/s\n60\/s × 8 ms                      = 480 ms\/s   →  48 % of one Coral<\/code><\/pre>\n
A factor of 1.5 regions per frame is a reasonable working figure for mixed outdoor views; a busy street needs two or three, a quiet corridor barely one. The figure is worth measuring rather than assuming, because it is the only one in the line that cannot be read off a data sheet.<\/p>\n
Why the Average Is the Wrong Number<\/h2>\n
Sizing against the average produces a system that works beautifully for months. The average is low because most cameras see nothing most of the time. A budget built on it has no reserve for the afternoon when a delivery arrives, the dog is in the garden and the wind picks up – three cameras busy in the same second, in the moment that matters most.<\/p>\n
The useful sizing case is the simultaneous one: every camera that could plausibly be busy at once, all busy, at the configured frame rate. If that case fits inside one second of TPU time, the installation has no bad afternoons. If it does not, the failure is invisible in the logs and obvious in the missing clip.<\/p>\n
What Each Detector Delivers<\/h2>\n\n\n\n\n\n\n\n\n\n
Detector<\/th>\nInference time<\/th>\nInferences\/s<\/th>\nCameras at 5 fps<\/th>\n<\/tr>\n<\/thead>\n
Coral M.2 \/ PCIe<\/td>\n~7 ms<\/td>\n143<\/td>\n19<\/td>\n<\/tr>\n
Coral USB on a USB 3.0 port<\/td>\n~8 ms<\/td>\n125<\/td>\n16<\/td>\n<\/tr>\n
Coral USB on a USB 2.0 port<\/td>\n~22 ms<\/td>\n45<\/td>\n6<\/td>\n<\/tr>\n
OpenVINO on an Intel iGPU<\/td>\n~15 ms<\/td>\n66<\/td>\n8<\/td>\n<\/tr>\n
CPU only, four cores<\/td>\n~90 ms<\/td>\n11<\/td>\n1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
The third row is the most common self-inflicted wound: the same stick, the same model, the same code, plugged into a black port instead of a blue one and running at a third of its speed. The last row explains why the CPU detector exists as a proof of concept rather than as an option – one camera is not an installation.<\/p>\n
Two accelerators roughly double the ceiling, because Frigate hands work to whichever detector is free. What they do not halve is everything described in the next section.<\/p>\n
The Bottleneck Is Usually Not the TPU<\/h2>\n
Before a frame reaches the detector it has to be decoded, and decoding is pure CPU work that no accelerator in this budget touches. Decoding a 1280×720 substream at 5 fps is small change. Decoding a 4K main stream at 20 fps costs roughly a core per camera, which is why an eight-camera setup can flatten a machine while the TPU sits at eleven per cent.<\/p>\n
The arrangement that avoids this is the standard one and worth stating plainly: detection runs on the substream, at the resolution the camera already produces, at five frames per second. Recording runs on the main stream and is copied, not re-encoded – the full resolution lands on disk without a single frame being decoded for it. The detector never sees 4K, and it never needs to: a person is found from a few hundred pixels of outline, and the clip that gets watched afterwards is the untouched main stream.<\/p>\n
How Saturation Announces Itself<\/h2>\n
Frigate reports two numbers that settle the question. Inference speed is the milliseconds one detection takes; if it climbs above the value from the table, the accelerator is throttling – a USB Coral warm enough to slow down is a familiar sight in a closed cabinet. Detection fps is the rate actually achieved; if it sits below the configured detect fps, frames are being queued and dropped.<\/p>\n
Both symptoms present the same way from the outside, and it is the least helpful presentation imaginable: the recording exists, the timeline is complete, and the event simply has no object attached to it. Nothing failed loudly. The budget was overdrawn for four seconds, and those four seconds were the ones worth having.<\/p>\n","protected":false},"excerpt":{"rendered":"
A Coral has 1000 milliseconds per second and spends about eight of them per detection. Everything else – camera count, detect fps, motion regions – is arithmetic that fits on one line.<\/p>\n","protected":false},"author":1,"featured_media":15152,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[91398,91181],"class_list":["post-10356","post","type-post","status-publish","format-standard","hentry","category-cctv","tag-monitoring-cctv-pl","tag-raspberry-pi"],"yoast_head":"\nFrigate and Coral TPU: How Many Cameras One Accelerator Can Handle - Lukas Wojcik - Blog<\/title>\n<meta name=\"description\" content=\"How many cameras one Coral TPU carries: inferences per second, inference time, and why the decoding CPU usually runs out before the accelerator does.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.lukaswojcik.com\/blog\/en\/cctv\/inference-budget-cameras-frame-rate-against-one-tpu\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Frigate and Coral TPU: How Many Cameras One Accelerator Can Handle - 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