127 lines
8.6 KiB
YAML
127 lines
8.6 KiB
YAML
%YAML:1.0
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####################################################################################################
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# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
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#
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# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
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# property and proprietary rights in and to this material, related
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# documentation and any modifications thereto. Any use, reproduction,
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# disclosure or distribution of this material and related documentation
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# without an express license agreement from NVIDIA CORPORATION or
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# its affiliates is strictly prohibited.
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####################################################################################################
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BaseConfig:
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minDetectorConfidence: 0.1894 # If the confidence of a detector bbox is lower than this, then it won't be considered for tracking
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TargetManagement:
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enableBboxUnClipping: 1 # In case the bbox is likely to be clipped by image border, unclip bbox
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preserveStreamUpdateOrder: 0 # When assigning new target ids, preserve input streams' order to keep target ids in a deterministic order over multuple runs
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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
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# [Creation & Termination Policy]
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minIouDiff4NewTarget: 0.3686 # 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.
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minTrackerConfidence: 0.1513 # 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]
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probationAge: 2 # If the target's age exceeds this, the target will be considered to be valid.
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maxShadowTrackingAge: 42 # Max length of shadow tracking. If the shadowTrackingAge exceeds this limit, the tracker will be terminated.
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earlyTerminationAge: 1 # If the shadowTrackingAge reaches this threshold while in TENTATIVE period, the target will be terminated prematurely.
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TrajectoryManagement:
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useUniqueID: 0 # Use 64-bit long Unique ID when assignining tracker ID. Default is [true]
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enableReAssoc: 1 # Enable Re-Assoc
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# [Re-Assoc Metric: Thresholds for valid candidates]
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minMatchingScore4Overall: 0.6622 # min matching score for overall
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minTrackletMatchingScore: 0.2940 # min tracklet similarity score for re-assoc
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minMatchingScore4ReidSimilarity: 0.0771 # min reid similarity score for re-assoc
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# [Re-Assoc Metric: Weights]
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matchingScoreWeight4TrackletSimilarity: 0.7981 # weight for tracklet similarity score
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matchingScoreWeight4ReidSimilarity: 0.3848 # weight for reid similarity score
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# [Re-Assoc: Motion-based]
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minTrajectoryLength4Projection: 34 # min trajectory length required to make projected trajectory
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prepLength4TrajectoryProjection: 58 # the length of the trajectory during which the state estimator is updated to make projections
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trajectoryProjectionLength: 33 # the length of the projected trajectory
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maxAngle4TrackletMatching: 67 # max angle difference for tracklet matching [degree]
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minSpeedSimilarity4TrackletMatching: 0.0574 # min speed similarity for tracklet matching
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minBboxSizeSimilarity4TrackletMatching: 0.1013 # min bbox size similarity for tracklet matching
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maxTrackletMatchingTimeSearchRange: 27 # the search space in time for max tracklet similarity
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trajectoryProjectionProcessNoiseScale: 0.0100 # trajectory projector's process noise scale w.r.t. state estimator
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trajectoryProjectionMeasurementNoiseScale: 100 # trajectory projector's measurement noise scale w.r.t. state estimator
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trackletSpacialSearchRegionScale: 0.0100 # the search region scale for peer tracklet
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# [Re-Assoc: Reid based. Reid model params are set in ReID section]
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reidExtractionInterval: 8 # frame interval to extract reid features per target
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DataAssociator:
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dataAssociatorType: 0 # the type of data associator among { DEFAULT= 0 }
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associationMatcherType: 1 # the type of matching algorithm among { GREEDY=0, CASCADED=1 }
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checkClassMatch: 1 # If checked, only the same-class objects are associated with each other. Default: true
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# [Association Metric: Thresholds for valid candidates]
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minMatchingScore4Overall: 0.0222 # Min total score
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minMatchingScore4SizeSimilarity: 0.3552 # Min bbox size similarity score
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minMatchingScore4Iou: 0.0548 # Min IOU score
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minMatchingScore4VisualSimilarity: 0.5043 # Min visual similarity score
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# [Association Metric: Weights]
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matchingScoreWeight4VisualSimilarity: 0.3951 # Weight for the visual similarity (in terms of correlation response ratio)
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matchingScoreWeight4SizeSimilarity: 0.6003 # Weight for the Size-similarity score
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matchingScoreWeight4Iou: 0.4033 # Weight for the IOU score
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# [Association Metric: Tentative detections] only uses iou similarity for tentative detections
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tentativeDetectorConfidence: 0.1024 # If a detection's confidence is lower than this but higher than minDetectorConfidence, then it's considered as a tentative detection
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minMatchingScore4TentativeIou: 0.2852 # Min iou threshold to match targets and tentative detection
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StateEstimator:
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stateEstimatorType: 1 # the type of state estimator among { DUMMY=0, SIMPLE=1, REGULAR=2 }
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# [Dynamics Modeling]
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processNoiseVar4Loc: 6810.8668 # Process noise variance for bbox center
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processNoiseVar4Size: 1541.8647 # Process noise variance for bbox size
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processNoiseVar4Vel: 1348.4874 # Process noise variance for velocity
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measurementNoiseVar4Detector: 100.0000 # Measurement noise variance for detector's detection
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measurementNoiseVar4Tracker: 293.3238 # Measurement noise variance for tracker's localization
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VisualTracker:
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visualTrackerType: 1 # the type of visual tracker among { DUMMY=0, NvDCF=1 }
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# [NvDCF: Feature Extraction]
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useColorNames: 1 # Use ColorNames feature
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useHog: 1 # Use Histogram-of-Oriented-Gradient (HOG) feature
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featureImgSizeLevel: 3 # Size of a feature image. Valid range: {1, 2, 3, 4, 5}, from the smallest to the largest
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featureFocusOffsetFactor_y: -0.1054 # 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
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# [NvDCF: Correlation Filter]
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filterLr: 0.0767 # learning rate for DCF filter in exponential moving average. Valid Range: [0.0, 1.0]
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filterChannelWeightsLr: 0.0339 # learning rate for the channel weights among feature channels. Valid Range: [0.0, 1.0]
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gaussianSigma: 0.5687 # Standard deviation for Gaussian for desired response when creating DCF filter [pixels]
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ReID:
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reidType: 2 # The type of reid among { DUMMY=0, NvDEEPSORT=1, Reid based reassoc=2, both NvDEEPSORT and reid based reassoc=3}
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# [Reid Network Info]
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batchSize: 100 # Batch size of reid network
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workspaceSize: 1000 # Workspace size to be used by reid engine, in MB
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reidFeatureSize: 256 # Size of reid feature
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reidHistorySize: 100 # Max number of reid features kept for one object
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inferDims: [3, 256, 128] # Reid network input dimension CHW or HWC based on inputOrder
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networkMode: 1 # Reid network inference precision mode among {fp32=0, fp16=1, int8=2 }
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# [Input Preprocessing]
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inputOrder: 0 # Reid network input order among { NCHW=0, NHWC=1 }. Batch will be converted to the specified order before reid input.
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colorFormat: 0 # Reid network input color format among {RGB=0, BGR=1 }. Batch will be converted to the specified color before reid input.
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offsets: [123.6750, 116.2800, 103.5300] # Array of values to be subtracted from each input channel, with length equal to number of channels
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netScaleFactor: 0.01735207 # Scaling factor for reid network input after substracting offsets
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keepAspc: 1 # Whether to keep aspc ratio when resizing input objects for reid
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# [Output Postprocessing]
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addFeatureNormalization: 1 # If reid feature is not normalized in network, adding normalization on output so each reid feature has l2 norm equal to 1
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minVisibility4GalleryUpdate: 0.6 # Add ReID embedding to the gallery only if the visibility is not lower than this
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# [Paths and Names]
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tltEncodedModel: "/opt/nvidia/deepstream/deepstream/samples/models/Tracker/resnet50_market1501.etlt" # NVIDIA TAO model path
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tltModelKey: "nvidia_tao" # NVIDIA TAO model key
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modelEngineFile: "/opt/nvidia/deepstream/deepstream/samples/models/Tracker/resnet50_market1501.etlt_b100_gpu0_fp16.engine" # Engine file path
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