When a camera rig records only on a trigger, should every camera have its own, or should one wake the rest?

research/capture-wake

When a camera rig records only on a trigger, should every camera have its own, or should one wake the rest? Four standby cameras with their own triggers use 80 Wh of 320 Wh continuous but get all four angles on only 68% of interesting seconds; one sentinel waking the others costs the same and gives 90.5%, and any-camera wake gives 100% for 92 Wh but its false-alarm energy grows with detector count. Assumed device figures.

9 tests

What it found

A synthetic 16 h day with assumed device figures and four standby cameras. With 6 thirty-second moments an hour and a 10% miss per detector, a trigger per camera uses 80 Wh (of 320 Wh continuous) but puts all four angles on only 68% of the interesting seconds, since independent misses differ by camera (≈0.9⁴). One sentinel waking the rest costs the same 80 Wh and gives 90.5%; waking all on any camera's trigger gives 100% for 92 Wh but its false-alarm energy grows with the number of detectors (281 against 126 Wh at 40 false alarms an hour). Sleeping the other cameras halves the rig to 41.5 Wh at the cost of the first 7 s of each moment. If the misses are shared, extra detectors buy nothing. Stylised: every figure assumed, not measured.

A figure from its own output

Figure produced by research/capture-wake: energy — capture-wake/experiments/energy.svg, written by the project's own experiment code.

Paper, code and results

More in Autonomous multi-angle capture

Where in a camera ring should the sentinel go when the subject faces a favourite direction?Where in a camera ring should the sentinel go when the subject faces a favourite direction? A sentinel facing the favourite direction misses 8.6% of moments against 34.4% for the one behind; spread cameras' misses are anti-correlated, so capture-wake's independence assumption overstates the all-miss rate by 2x on a ring of 4 and understates it 12x for a tight cluster. With the turned-face miss measured in capture-detectors (54 faces) in place of the assumed curve, placement matters only if the back of a head goes undetected (untested), and weak detectors miss 8 to 45% of moments even with four cameras listening.
Does a longer window, a lower gamma or a delay prior fix the period aliasing and early-start failures of GCC audio sync on real clips?Does a longer window or a delay prior fix GCC sync's aliasing on real clips? Window length (shared audio) is the lever: 97% wrong at 1 s, 38% at 2 s, 16% at 3 s on the failing clips; a tone clip needs a prior tighter than its period.
How long a window of sound does it take to sync two drifting cameras, and how often must you redo it?How long a window of sound syncs two drifting cameras? GCC-PHAT plus a robust line fit syncs to under 0.02 ms over an hour from -10 dB SNR with 2 s windows; a sync made once at the start drifts one frame in 833 s at 40 ppm.
How small can a face or a person be before a real detector stops finding it?How small can a face be before a real detector misses it? YuNet found every face of 16 px or wider and half at 10 px (assumed: 20), cutting the modelled blur leak on an 8K 360 camera from 4.8% to 0.01%; YOLOX finds half of people at 23 px tall. A pilot on 22 easy faces in 128 COCO photos: an optimistic bound.
Does a predictive gap-seeking planner recover the framing a follow camera loses when it avoids pedestrians?Does a predictive planner recover the framing a follow camera loses by avoiding pedestrians? Not at equal settings (drone 58% framed vs 68% for repulsion at 0.1 people/m²), but it halves-to-tenths the contacts (36 vs 161 per hour; wheeled 21 vs 326) and moves the framing/contacts front left. 2-D simulation, assumed platform figures.
How much of a predictive follow camera's contact reduction survives noisy, delayed pedestrian detections?How much of a predictive follow camera's contact reduction survives noisy, delayed pedestrian detections? Framing is unchanged (57-61%) but a drone's contacts rise from 36 per hour with exact data to 120 at 0.3 m noise and 0.25 s delay, and 373 at 1 m and 1 s (ignoring pedestrians: 523). 2-D simulation, swept noise, perfect association assumed.

All 34 Autonomous multi-angle capture projects →

Other research topics

Proper scoring & elicitation28 Effort & peer prediction9 Verification games & slashing19 Detection & tolerances14 Markets as routers13 Prediction-market design9 Paying contributors8 Decentralised training systems23 Digital twins & sim-to-real164 LLM chip design loops40 Robot manufacturing loops53 Ambient AI assistant46 Self-replicating manufacturing34 Simulation-first design64 Engineered clothing45 Task-specific AI accelerators40 Algorithmic chip-design loops42 Open-source purpose-built robots44 The software-engineering loop43 Adversarial human imitation41 Open-source residential construction41 Autonomous multi-angle capture34 Sensing, imaging and 3D31 Energy supply and dense energy storage39 Robot societies among people31 Agentic co-design of low-cost robots27 Laundry-hanging robots25 Machine learning on qualitative data23 Robots on the building site27 Civilizations & group psychology1 History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.