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# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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BaseConfig:
  minDetectorConfidence: 0   # 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
  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: 0.5   # 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: 0.2   # 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: 3 # If the target's age exceeds this, the target will be considered to be valid.
  maxShadowTrackingAge: 30  # 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. Default is [true]

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

  # [Association Metric: Thresholds for valid candidates]
  minMatchingScore4Overall: 0.0   # Min total score
  minMatchingScore4SizeSimilarity: 0.6  # Min bbox size similarity score
  minMatchingScore4Iou: 0.0       # Min IOU score
  minMatchingScore4VisualSimilarity: 0.7  # Min visual similarity score

  # [Association Metric: Weights]
  matchingScoreWeight4VisualSimilarity: 0.6  # Weight for the visual similarity (in terms of correlation response ratio)
  matchingScoreWeight4SizeSimilarity: 0.0    # Weight for the Size-similarity score
  matchingScoreWeight4Iou: 0.4   # Weight for the IOU score

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

  # [Dynamics Modeling]
  processNoiseVar4Loc: 2.0    # Process noise variance for bbox center
  processNoiseVar4Size: 1.0   # Process noise variance for bbox size
  processNoiseVar4Vel: 0.1    # Process noise variance for velocity
  measurementNoiseVar4Detector: 4.0    # Measurement noise variance for detector's detection
  measurementNoiseVar4Tracker: 16.0    # 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: -0.2 # 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: 0.075 # learning rate for DCF filter in exponential moving average. Valid Range: [0.0, 1.0]
  filterChannelWeightsLr: 0.1 # learning rate for the channel weights among feature channels. Valid Range: [0.0, 1.0]
  gaussianSigma: 0.75 # Standard deviation for Gaussian for desired response when creating DCF filter [pixels]
