How much of a predictive follow camera's contact reduction survives noisy, delayed pedestrian detections?
research/capture-followtrack
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.
What it found
capture-followplan's planner cut a drone's close passes (within 0.5 m) at 0.1 people/m² from 523 to 36 per hour, but it was handed exact pedestrian positions and velocities. Here a constant-velocity Kalman tracker over detections with Gaussian noise (0.1 to 1.0 m) and latency (0 to 1 s) feeds it instead, on the same 8 crowds. Framing is unchanged (57 to 61%, against 58.0% with exact data); contacts are not: 45 at 0.1 m noise and no delay, 120 at 0.3 m and 0.25 s, 199 to 257 at 0.6 m, 373 at 1.0 m and 1 s. The planner beats exact-data repulsion (161) only while noise is about 0.3 m or less and latency under about 0.5 s, and that comparison favours repulsion, which was not rerun with noisy inputs. Assumed, not measured: perfect detection-to-track association, independent Gaussian noise, the filter's process noise, and capture-follow’s round-number platform figures; the noise levels are a sweep, not a detector’s datasheet.
A figure from its own output
Paper, code and results
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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.