FaceRecognition/data/tracker_configs/config_tracker_NvDeepSORT.yml
2025-07-26 07:08:46 +00:00

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# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
####################################################################################################
BaseConfig:
minDetectorConfidence: 0.0762 # If the confidence of a detector bbox is lower than this, then it won't be considered for tracking
TargetManagement:
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: 0.9847 # 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: 0.4314 # 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: 68 # 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 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: Mahalanobis distance threshold (refer to DeepSORT paper) ]
thresholdMahalanobis: 12.1875 # Threshold of Mahalanobis distance. A detection and a target are not matched if their distance is larger than the threshold.
# [Association Metric: Thresholds for valid candidates]
minMatchingScore4Overall: 0.1794 # Min total score
minMatchingScore4SizeSimilarity: 0.3291 # Min bbox size similarity score
minMatchingScore4Iou: 0.2364 # Min IOU score
minMatchingScore4ReidSimilarity: 0.7505 # Min reid similarity score
# [Association Metric: Weights for valid candidates]
matchingScoreWeight4SizeSimilarity: 0.7178 # Weight for the Size-similarity score
matchingScoreWeight4Iou: 0.4551 # Weight for the IOU score
matchingScoreWeight4ReidSimilarity: 0.3197 # Weight for the reid similarity
# [Association Metric: Tentative detections] only uses iou similarity for tentative detections
tentativeDetectorConfidence: 0.2479 # If a detection's confidence is lower than this but higher than minDetectorConfidence, then it's considered as a tentative detection
minMatchingScore4TentativeIou: 0.2376 # Min iou threshold to match targets and tentative detection
StateEstimator:
stateEstimatorType: 2 # the type of state estimator among { DUMMY=0, SIMPLE=1, REGULAR=2 }
# [Dynamics Modeling]
noiseWeightVar4Loc: 0.0503 # weight of process and measurement noise for bbox center; if set, location noise will be proportional to box height
noiseWeightVar4Vel: 0.0037 # weight of process and measurement noise for velocity; if set, velocity noise will be proportional to box height
useAspectRatio: 1 # use aspect ratio in Kalman filter's observation
ReID:
reidType: 1 # The type of reid among { DUMMY=0, DEEP=1 }
# [Reid Network Info]
batchSize: 100 # Batch size of reid network
workspaceSize: 1000 # Workspace size to be used by reid engine, in MB
reidFeatureSize: 256 # Size of reid feature
reidHistorySize: 100 # Max number of reid features kept for one object
inferDims: [3, 256, 128] # Reid network input dimension CHW or HWC based on inputOrder
networkMode: 1 # Reid network inference precision mode among {fp32=0, fp16=1, int8=2 }
# [Input Preprocessing]
inputOrder: 0 # Reid network input order among { NCHW=0, NHWC=1 }. Batch will be converted to the specified order before reid input.
colorFormat: 0 # Reid network input color format among {RGB=0, BGR=1 }. Batch will be converted to the specified color before reid input.
offsets: [123.6750, 116.2800, 103.5300] # Array of values to be subtracted from each input channel, with length equal to number of channels
netScaleFactor: 0.01735207 # Scaling factor for reid network input after substracting offsets
keepAspc: 1 # Whether to keep aspc ratio when resizing input objects for reid
# [Output Postprocessing]
addFeatureNormalization: 1 # If reid feature is not normalized in network, adding normalization on output so each reid feature has l2 norm equal to 1
# [Paths and Names]
tltEncodedModel: "/opt/nvidia/deepstream/deepstream/samples/models/Tracker/resnet50_market1501.etlt" # NVIDIA TAO model path
tltModelKey: "nvidia_tao" # NVIDIA TAO model key
modelEngineFile: "/opt/nvidia/deepstream/deepstream/samples/models/Tracker/resnet50_market1501.etlt_b100_gpu0_fp16.engine" # Engine file path