Optimize auto-track for stationary camera: Gaussian blur + confidence threshold - #68
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… threshold For a fixed (non-panning) camera the background is static between frames, making per-frame sensor noise and compression artefacts the dominant source of false template matches. Changes: - Apply a 5×5 Gaussian blur to both the template at capture time and the search region on every tracking call. Using the same filter in both places keeps TM_CCOEFF_NORMED scores accurate while suppressing the high-frequency noise that would otherwise produce spurious match peaks. - Return the TM_CCOEFF_NORMED confidence score (maxVal) from the worker with every track-result message. - Reject matches below a 0.25 confidence threshold in OpenCVTracker.track() to drop motion-blurred or heavily compressed frames without discarding genuine but imperfect matches. - Keep SEARCH_PADDING_FACTOR at 2, which is appropriate when the object's own motion is the sole source of inter-frame displacement. https://claude.ai/code/session_01GJwcYz3EPjBY1rENbNR6MJ
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For a fixed (non-panning) camera the background is static between frames,
making per-frame sensor noise and compression artefacts the dominant source
of false template matches.
Changes:
search region on every tracking call. Using the same filter in both
places keeps TM_CCOEFF_NORMED scores accurate while suppressing the
high-frequency noise that would otherwise produce spurious match peaks.
every track-result message.
to drop motion-blurred or heavily compressed frames without discarding
genuine but imperfect matches.
own motion is the sole source of inter-frame displacement.
https://claude.ai/code/session_01GJwcYz3EPjBY1rENbNR6MJ