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# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
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BaseConfig:
  minDetectorConfidence: 0.0430    # If the confidence of a detector bbox is lower than this, then it won't be considered for tracking

TargetManagement:
  enableBboxUnClipping: 1    # In case the bbox is likely to be clipped by image border, unclip bbox
  preserveStreamUpdateOrder: 0    # When assigning new target ids, preserve input streams' order to keep target ids in a deterministic order over multuple runs
  maxTargetsPerStream: 150    # Max number of targets to track per stream. Recommended to set >10. Note: this value should account for the targets being tracked in shadow mode as well. Max value depends on the GPU memory capacity

  # [Creation & Termination Policy]
  minIouDiff4NewTarget: 0.7418    # If the IOU between the newly detected object and any of the existing targets is higher than this threshold, this newly detected object will be discarded.
  minTrackerConfidence: 0.4009    # If the confidence of an object tracker is lower than this on the fly, then it will be tracked in shadow mode. Valid Range: [0.0, 1.0]
  probationAge: 2    # If the target's age exceeds this, the target will be considered to be valid.
  maxShadowTrackingAge: 51    # Max length of shadow tracking. If the shadowTrackingAge exceeds this limit, the tracker will be terminated.
  earlyTerminationAge: 1    # If the shadowTrackingAge reaches this threshold while in TENTATIVE period, the target will be terminated prematurely.

TrajectoryManagement:
  useUniqueID: 0    # Use 64-bit long Unique ID when assignining tracker ID.

DataAssociator:
  dataAssociatorType: 0    # the type of data associator among { DEFAULT= 0 }
  associationMatcherType: 1    # the type of matching algorithm among { GREEDY=0, CASCADED=1 }
  checkClassMatch: 1    # If checked, only the same-class objects are associated with each other. Default: true

  # [Association Metric: Thresholds for valid candidates]
  minMatchingScore4Overall: 0.4290    # Min total score
  minMatchingScore4SizeSimilarity: 0.3627    # Min bbox size similarity score
  minMatchingScore4Iou: 0.2575    # Min IOU score
  minMatchingScore4VisualSimilarity: 0.5356    # Min visual similarity score

  # [Association Metric: Weights]
  matchingScoreWeight4VisualSimilarity: 0.3370    # Weight for the visual similarity (in terms of correlation response ratio)
  matchingScoreWeight4SizeSimilarity: 0.4354    # Weight for the Size-similarity score
  matchingScoreWeight4Iou: 0.3656    # Weight for the IOU score

  # [Association Metric: Tentative detections] only uses iou similarity for tentative detections
  tentativeDetectorConfidence: 0.2008    # If a detection's confidence is lower than this but higher than minDetectorConfidence, then it's considered as a tentative detection
  minMatchingScore4TentativeIou: 0.5296    # Min iou threshold to match targets and tentative detection

StateEstimator:
  stateEstimatorType: 1    # the type of state estimator among { DUMMY=0, SIMPLE=1, REGULAR=2 }

  # [Dynamics Modeling]
  processNoiseVar4Loc: 1.5110    # Process noise variance for bbox center
  processNoiseVar4Size: 1.3159    # Process noise variance for bbox size
  processNoiseVar4Vel: 0.0300    # Process noise variance for velocity
  measurementNoiseVar4Detector: 3.0283    # Measurement noise variance for detector's detection
  measurementNoiseVar4Tracker: 8.1505    # Measurement noise variance for tracker's localization

VisualTracker:
  visualTrackerType: 1    # the type of visual tracker among { DUMMY=0, NvDCF=1 }

  # [NvDCF: Feature Extraction]
  useColorNames: 1    # Use ColorNames feature
  useHog: 0    # Use Histogram-of-Oriented-Gradient (HOG) feature
  featureImgSizeLevel: 2    # Size of a feature image. Valid range: {1, 2, 3, 4, 5}, from the smallest to the largest
  featureFocusOffsetFactor_y: -0.2000    # The offset for the center of hanning window relative to the feature height. The center of hanning window would move by (featureFocusOffsetFactor_y*featureMatSize.height) in vertical direction

  # [NvDCF: Correlation Filter]
  filterLr: 0.0750    # learning rate for DCF filter in exponential moving average. Valid Range: [0.0, 1.0]
  filterChannelWeightsLr: 0.1000    # learning rate for the channel weights among feature channels. Valid Range: [0.0, 1.0]
  gaussianSigma: 0.7500    # Standard deviation for Gaussian for desired response when creating DCF filter [pixels]
