Is this expected? Is there any way to move this loading time to earlier so I don’t hold up processing waiting for it to complete once we start receiving frames?
I am running this for my custom application but can attempt to reproduce with an existing sample if this is not expected.
Also, here is the NVSORT_custom_template.yml contents
BaseConfig:
# If the confidence of a detector bbox is lower than this, then it won't be
# considered for tracking
minDetectorConfidence: 0.2
TargetManagement:
# In case the bbox is likely to be clipped by image border, unclip bbox
enableBboxUnClipping: 0
# 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
maxTargetsPerStream: 100
#
# [Creation & Termination Policy]
#
# 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.
minIouDiff4NewTarget: 0.26006486048167077
# 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]
minTrackerConfidence: 0.008134201107677205
# If the target's age exceeds this, the target will be considered to be valid.
probationAge: 1
# Max length of shadow tracking. If the shadowTrackingAge exceeds this limit,
# the tracker will be terminated.
maxShadowTrackingAge: 5
# If the shadowTrackingAge reaches this threshold while in TENTATIVE period,
# the target will be terminated prematurely.
earlyTerminationAge: 1
TrajectoryManagement:
# Use 64-bit long Unique ID when assignining tracker ID. Default is [true]
useUniqueID: 1
DataAssociator:
# the type of data associator among { DEFAULT= 0 }
dataAssociatorType: 0
# the type of matching algorithm among { GREEDY=0, CASCADED=1 }
associationMatcherType: 0
# If checked, only the same-class objects are associated with each other.
# Default: true
checkClassMatch: 1
#
# [Association Metric: Thresholds for valid candidates]
#
# Min total score
minMatchingScore4Overall: 0.0026861559707920825
# Min bbox size similarity score
minMatchingScore4SizeSimilarity: 0.1 # Size similarity in our case is important
# Min IOU score
minMatchingScore4Iou: 0.006837245935615654
# Weight for the Size-similarity score
matchingScoreWeight4SizeSimilarity: 0.1
# Weight for the IOU score
matchingScoreWeight4Iou: 0.04708585446530863
#
# [Association Metric: Tentative detections] only uses iou similarity for
# tentative detections
#
# If a detection's confidence is lower than this but higher than
# minDetectorConfidence, then it's considered as a tentative detection
tentativeDetectorConfidence: 0.7628657651628549
# Min iou threshold to match targets and tentative detection
minMatchingScore4TentativeIou: 0.08268307009366113
# use the predicted state info for association instead of the past known
# states
usePrediction4Assoc: 1
StateEstimator:
# the type of state estimator among { DUMMY=0, SIMPLE=1, REGULAR=2 }
stateEstimatorType: 2
#
# [Dynamics Modeling]
#
# weight of process and measurement noise for bbox center; if set, location
# noise will be proportional to box height
noiseWeightVar4Loc: 0.13762054930701298
# weight of process and measurement noise for velocity; if set, velocity noise
# will be proportional to box height
noiseWeightVar4Vel: 0.01 # Presumably much less noise compared to water
# use aspect ratio in Kalman filter's observation
useAspectRatio: 0
