FeatureTrackerConfig
This message is used to configure the FeatureTracker node. You can set the CornerDetector, FeatureMaintainer and MotionEstimator.Reference
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depthai.FeatureTrackerConfig(depthai.Buffer)
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CornerDetector
Corner detector configuration structure.
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FeatureMaintainer
FeatureMaintainer configuration structure.
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MotionEstimator
Used for feature reidentification between current and previous features.
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get(self) -> RawFeatureTrackerConfig: RawFeatureTrackerConfigRetrieve configuration data for FeatureTracker. Returns: config for feature tracking algorithm
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set(self, config: RawFeatureTrackerConfig) -> FeatureTrackerConfig: FeatureTrackerConfigSet explicit configuration. Parameter ``config``: Explicit configuration
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setHwMotionEstimation(self) -> FeatureTrackerConfig: FeatureTrackerConfigSet hardware accelerated motion estimation using block matching. Faster than optical flow (software implementation) but might not be as accurate.
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setNumTargetFeatures(self, numTargetFeatures: int) -> FeatureTrackerConfig: FeatureTrackerConfigSet number of target features to detect. Parameter ``numTargetFeatures``: Number of features
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depthai.FeatureTrackerConfig.CornerDetector
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Thresholds
Threshold settings structure for corner detector.
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Type
Members: HARRIS SHI_THOMASI
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cellGridDimension
Ensures distributed feature detection across the image. Image is divided into horizontal and vertical cells, each cell has a target feature count = numTargetFeatures / cellGridDimension. Each cell has its own feature threshold. A value of 4 means that the image is divided into 4x4 cells of equal width/height. Maximum 4, minimum 1.
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enableSobel
Enable 3x3 Sobel operator to smoothen the image whose gradient is to be computed. If disabled, a simple 1D row/column differentiator is used for gradient.
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enableSorting
Enable sorting detected features based on their score or not.
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numMaxFeatures
Hard limit for the maximum number of features that can be detected. 0 means auto, will be set to the maximum value based on memory constraints.
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numTargetFeatures
Target number of features to detect. Maximum number of features is determined at runtime based on algorithm type.
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thresholds
Threshold settings. These are advanced settings, suitable for debugging/special cases.
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type
Corner detector algorithm type.
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depthai.FeatureTrackerConfig.CornerDetector.Thresholds
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decreaseFactor
When detected number of features exceeds the maximum in a cell threshold is lowered by multiplying its value with this factor.
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increaseFactor
When detected number of features doesn't exceed the maximum in a cell, threshold is increased by multiplying its value with this factor.
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initialValue
Minimum strength of a feature which will be detected. 0 means automatic threshold update. Recommended so the tracker can adapt to different scenes/textures. Each cell has its own threshold. Empirical value.
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max
Maximum limit for threshold. Applicable when automatic threshold update is enabled. 0 means auto. Empirical value.
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min
Minimum limit for threshold. Applicable when automatic threshold update is enabled. 0 means auto, 6000000 for HARRIS, 1200 for SHI_THOMASI. Empirical value.
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depthai.FeatureTrackerConfig.CornerDetector.Type
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depthai.FeatureTrackerConfig.FeatureMaintainer
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enable
Enable feature maintaining or not.
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lostFeatureErrorThreshold
Optical flow measures the tracking error for every feature. If the point can’t be tracked or it’s out of the image it will set this error to a maximum value. This threshold defines the level where the tracking accuracy is considered too bad to keep the point.
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minimumDistanceBetweenFeatures
Used to filter out detected feature points that are too close. Requires sorting enabled in detector. Unit of measurement is squared euclidean distance in pixels.
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trackedFeatureThreshold
Once a feature was detected and we started tracking it, we need to update its Harris score on each image. This is needed because a feature point can disappear, or it can become too weak to be tracked. This threshold defines the point where such a feature must be dropped. As the goal of the algorithm is to provide longer tracks, we try to add strong points and track them until they are absolutely untrackable. This is why, this value is usually smaller than the detection threshold.
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depthai.FeatureTrackerConfig.MotionEstimator
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OpticalFlow
Optical flow configuration structure.
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Members: LUCAS_KANADE_OPTICAL_FLOW HW_MOTION_ESTIMATION
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enable
Enable motion estimation or not.
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opticalFlow
Optical flow configuration. Takes effect only if MotionEstimator algorithm type set to LUCAS_KANADE_OPTICAL_FLOW.
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type
Motion estimator algorithm type.
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depthai.FeatureTrackerConfig.MotionEstimator.OpticalFlow
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epsilon
Feature tracking termination criteria. Optical flow will refine the feature position on each pyramid level until the displacement between two refinements is smaller than this value. Decreasing this number increases runtime.
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maxIterations
Feature tracking termination criteria. Optical flow will refine the feature position maximum this many times on each pyramid level. If the Epsilon criteria described in the previous chapter is not met after this number of iterations, the algorithm will continue with the current calculated value. Increasing this number increases runtime.
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pyramidLevels
Number of pyramid levels, only for optical flow. AUTO means it's decided based on input resolution: 3 if image width <= 640, else 4. Valid values are either 3/4 for VGA, 4 for 720p and above.
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searchWindowHeight
Image patch height used to track features. Must be an odd number, maximum 9. N means the algorithm will be able to track motion at most (N-1)/2 pixels in a direction per pyramid level. Increasing this number increases runtime
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searchWindowWidth
Image patch width used to track features. Must be an odd number, maximum 9. N means the algorithm will be able to track motion at most (N-1)/2 pixels in a direction per pyramid level. Increasing this number increases runtime
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depthai.FeatureTrackerConfig.MotionEstimator.Type
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