createBackgroundSubtractorKNN

Motion and trackingfunctionOpenCV 5.0.0

import { createBackgroundSubtractorKNN } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

Explore visually: KNN background subtraction →

Classify a pixel using how many nearby samples in its recent history agree with it.

VISUAL WALKTHROUGHMotion

REFERENCE INPUTCurrent frame

The object has moved away from its earlier location.

→

RESULTForeground mask

The moving region is retained in this ideal example. Startup, shadows and camera motion complicate real masks.

Illustrative example. The stages explain the method; they are not a live OpenCV execution.

Creates KNN Background Subtractor

createBackgroundSubtractorKNN(history: number, dist2Threshold: number, detectShadows: boolean): BackgroundSubtractorKNN | null;

4 available overloads

createBackgroundSubtractorKNN(): BackgroundSubtractorKNN | null;

createBackgroundSubtractorKNN(history: number): BackgroundSubtractorKNN | null;

createBackgroundSubtractorKNN(history: number, dist2Threshold: number): BackgroundSubtractorKNN | null;

createBackgroundSubtractorKNN(history: number, dist2Threshold: number, detectShadows: boolean): BackgroundSubtractorKNN | null;

history

Length of the history.

dist2Threshold

Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update.

detectShadows

If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

Returns

The BackgroundSubtractorKNN | null result.

Upstream declaration ↗

These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.