Negative confidence in DP 6.1.1

Please provide complete information as applicable to your setup.

**• Hardware Platform (Jetson / GPU)
Jetson Xavier AGX
• DeepStream Version
6.1.1
• JetPack Version (valid for Jetson only)
5.0.2

HI,

I am relatively new in computer vision. I am using the python sample application with nvdsanalytics.

My pipeline consists of :

Pgie : trafficcamnet resnet 18. Etlt
Sgie vehicletypenet .etlt
…(rest like in the sample)

Now im extracting the obj_meta.confidence in the buffer method.
In some cases, the confidence is dropping on -1.0 .
It doesnt matter which cluster-mode i choose, the issue still persists. (Tried cluster-mode 1,2 and 3)
I found a topic earlier in this forum, where in DP 5.0 the issue should be fixed wothin the next patch.

What can i do now? I could build a src pad probe on pgie and ectract meta from there, but i think this would give me an performance loss and maybe problems to map/ connect metadata.

Is there any easy solution i am missing ?

CODE- Part:

while l_obj:
try:

              obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
          except StopIteration:
              break
          obj_counter[obj_meta.class_id] += 1
          l_user_meta = obj_meta.obj_user_meta_list
          confidence = obj_meta.confidence
          tr_confidence= obj_meta.tracker_confidence# Vertrauenswürdigkeitsinformationen


          label = f"Confidence: {confidence:.4f}%  tr-conf: {tr_confidence:.4f}"
          print(label)
          getty = pyds.get_string(obj_meta.text_params.display_text)
          obj_meta.text_params.display_text =getty + "  "+ label
           # Extract object level meta data from NvDsAnalyticsObjInfo

#############################################################

PGIE

gie-unique-id=1
tlt-model-key=tlt_encode
offsets=0.0;0.0;0.0
infer-dims=3;544;960
#force-implicit-batch-dim=1
network-type=0
network-mode=2
num-detected-classes=4
output-blob-names=output_cov/Sigmoid;output_bbox/BiasAdd
uff-input-blob-name=input_1
model-color-format=0
maintain-aspect-ratio=0
output-tensor-meta=0
enable-dla=1
use-dla-core=0
interval=3
process-mode=1
threshold =0.05
cluster-mode = 2
pre-cluster-threshold=0.2
group-threshold=0.5
dbscan-min-score = 0.7

SGIE:

gpu-id=0
net-scale-factor=1
offsets=103.939;116.779;123.68
tlt-model-key=tlt_encode
infer-dims=3;224;224
uff-input-blob-name=input_1
batch-size=4
process-mode=2
model-color-format=0
0=FP32, 1=INT8, 2=FP16 mode
network-mode=1
network-type=1 #1 für classifier
num-detected-classes=6
interval=0
operate-on-gie-id =1
operate-on-class-ids=0
gie-unique-id=2
output-blob-names=predictions/Softmax
classifier-threshold=0.2
classifier-async-mode=1

[tracker-config]:

BaseConfig:
minDetectorConfidence: -1 # 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: 0.1 # 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: 50 # 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: 0.4 # Min bbox size similarity score
minMatchingScore4Iou: 0.0 # Min IOU score
minMatchingScore4VisualSimilarity: 0.5 # 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 }

outputs:

case 1:
#######################################
deepstream | Frame Number= 119
deepstream | 5 objects :
deepstream | Confidence: 0.4648% tr-conf: 0.8164
deepstream | Confidence: 0.2081% tr-conf: 0.7645
deepstream | Confidence: 0.2432% tr-conf: 0.7071
deepstream | Confidence: 0.3403% tr-conf: 0.8785
deepstream | Confidence: 0.3550% tr-conf: 0.6047
#########################################
case 2:
#########################################
deepstream | Frame Number= 120
deepstream | 5 objects :
deepstream | Confidence: -0.1000% tr-conf: 0.7942
deepstream | Confidence: -0.1000% tr-conf: 0.7517
deepstream | Confidence: -0.1000% tr-conf: 0.6100
deepstream | Confidence: -0.1000% tr-conf: 0.8599
deepstream | Confidence: -0.1000% tr-conf: 0.9654
deepstream | Confidence: -0.1000% tr-conf: 0.9482
#########################################

Could you reproduce that with our deepstream-nvdsanalytics without any change?

sorry for late reply.

under following circumstances :

  • using deepstream- python-apps- 1.1.4 from Github -Repo

  • using deepstream-6.1.1- sample docker

  • using sample video 1080_h264

  • just adding the confidence output

i am not able to reproduce the error. The confidences were all above 0%.

EDIT :

i could reproduce the behavior with changing the sample model to the newest release of the trafficcamnet.etlt
i took the standard config.

When i change the network-mode now from int 8 to fp16 mode, i get the warning “subnormal fp16 values detected”.

is something wrong with the .etlt file? → i tested the previous 2 version. all have the same behavour.

Note:

it ist suspicious, that it’s always the second frame who spits out a negative number:

Frame Number= 1108 stream id= 0 Number of Objects= 12 Vehicle_count= 8 Person_count= 4
##################################################
##################################################
Confidence: -0.1000% tr-conf: 0.7590
Confidence: -0.1000% tr-conf: 0.6887
Confidence: -0.1000% tr-conf: 0.6692
Confidence: -0.1000% tr-conf: 0.7069
Object 443 moving in direction: North
Confidence: -0.1000% tr-conf: 0.7577
Confidence: -0.1000% tr-conf: 0.9387
Confidence: -0.1000% tr-conf: 0.9040
Confidence: -0.1000% tr-conf: 0.7647
Confidence: -0.1000% tr-conf: 0.8747
Confidence: -0.1000% tr-conf: 0.9245
Confidence: -0.1000% tr-conf: 0.6205
Confidence: -0.1000% tr-conf: 0.4610
Objs in ROI: {‘RF’: 0}
Linecrossing Cumulative: {‘Exit’: 18}
Linecrossing Current Frame: {‘Exit’: 0}
Frame Number= 1109 stream id= 0 Number of Objects= 12 Vehicle_count= 8 Person_count= 4
##################################################
##################################################
Confidence: 0.3661% tr-conf: 0.9117
Confidence: 0.9002% tr-conf: 0.7713
Confidence: 0.9189% tr-conf: 0.9132
Confidence: 0.9225% tr-conf: 0.8664
Confidence: 0.2872% tr-conf: 0.4777
Confidence: 0.4634% tr-conf: 0.7914
Confidence: 0.4788% tr-conf: 0.6532
Confidence: 0.4988% tr-conf: 0.6550
Confidence: 0.5404% tr-conf: 0.5583
Confidence: 0.5419% tr-conf: 0.7045
Confidence: 0.5571% tr-conf: 0.4691
Confidence: 0.6516% tr-conf: 0.9037
Confidence: 0.8745% tr-conf: 0.7135
Object 443 moving in direction: North
Confidence: 0.9359% tr-conf: 0.7017
Objs in ROI: {‘RF’: 0}
Linecrossing Cumulative: {‘Exit’: 18}
Linecrossing Current Frame: {‘Exit’: 0}
Frame Number= 1110 stream id= 0 Number of Objects= 14 Vehicle_count= 10 Person_count= 4
##################################################
##################################################
Confidence: -0.1000% tr-conf: 0.7124
Confidence: -0.1000% tr-conf: 0.7467
Confidence: -0.1000% tr-conf: 0.7364
Confidence: -0.1000% tr-conf: 0.6993
Object 443 moving in direction: North
Confidence: -0.1000% tr-conf: 0.7922
Confidence: -0.1000% tr-conf: 0.5150
Confidence: -0.1000% tr-conf: 0.9038
Confidence: -0.1000% tr-conf: 0.8943
Confidence: -0.1000% tr-conf: 0.4526
Confidence: -0.1000% tr-conf: 0.7941
Confidence: -0.1000% tr-conf: 0.9189
Confidence: -0.1000% tr-conf: 0.9169
Confidence: -0.1000% tr-conf: 0.6591
Confidence: -0.1000% tr-conf: 0.4569
Objs in ROI: {‘RF’: 0}
Linecrossing Cumulative: {‘Exit’: 18}
Linecrossing Current Frame: {‘Exit’: 0}

I use the DeepStream 6.3 docker and the latest trafficcamnet.etlt model. The config file is like: deepstream_app_source1_trafficcamnet.txt. It works well. You can try to update to our latest version.

Unfortunately, we use an edge device which only supports this version in productive.

We don’t provide patches separately for previous versions. So we suggest that you consider upgrading the version and get better service.

Hi yuweiw,

i could solve the issue. it is embarassing simple. the issue belongs to the interval settings in pgie. as is set the value to zero, the issue was “solved” :').

Thank you!

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