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* perf(track): avoid numpy reductions on tiny box lists in position smoothing update_position runs per tracked object per frame. While a position has fewer than 10 samples it calls np.percentile four times, and average_boxes (per stationary object per frame) calls np.mean four times - all on lists of at most 10 ints, where numpy's per-call dispatch/validation overhead dominates the actual work. Replace them with pure-Python equivalents: - average_boxes: sum()/len() instead of np.mean (bit-identical output) - interpolated_percentile(): linear-interpolated percentile matching numpy.percentile (including its lerp branch at frac>=0.5) for the small lists used here, in place of np.percentile Measured in the release image (numpy 1.26.4) on a 10-element list: np.percentile 18735 ns -> 191 ns/call (98x); np.mean-based average_boxes 7480 ns -> 591 ns (12.7x); ~74 us saved per object-frame in update_position. A live py-spy --gil profile of a camera process_frames worker showed np.percentile (update_position) and np.mean (average_boxes) among the top Frigate-owned on-CPU frames. Output is unchanged: added tests assert both helpers are bit-identical to numpy over randomized small inputs. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Drop interpolated_percentile, keep only average_boxes Per review: reimplementing np.percentile hurts readability and risks divergence from numpy (e.g. numpy 2.x). Revert update_position to np.percentile and remove the helper; keep only the average_boxes change (sum()/len() instead of np.mean), which stays bit-identical. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>