Video analytics

Please provide complete information as applicable to your setup.

Jetpack-4.5.1
deepstream-5.1
Ubentu 18.04 LTS
cuda version-10.2
TenserRT-7.1.3.1

Use Trafficcamnet model for detection please refere below pgie config file data and suggest me improve true detection(Miss incidents small size vehicles in view) and reduce false detection and detect 15 vehicles in zone than as queue event.
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Following properties are mandatory when engine files are not specified:

int8-calib-file(Only in INT8)

Caffemodel mandatory properties: model-file, proto-file, output-blob-names

UFF: uff-file, input-dims, uff-input-blob-name, output-blob-names

ONNX: onnx-file

Mandatory properties for detectors:

num-detected-classes

Optional properties for detectors:

cluster-mode(Default=Group Rectangles), interval(Primary mode only, Default=0)

custom-lib-path

parse-bbox-func-name

Mandatory properties for classifiers:

classifier-threshold, is-classifier

Optional properties for classifiers:

classifier-async-mode(Secondary mode only, Default=false)

Optional properties in secondary mode:

operate-on-gie-id(Default=0), operate-on-class-ids(Defaults to all classes),

input-object-min-width, input-object-min-height, input-object-max-width,

input-object-max-height

Following properties are always recommended:

batch-size(Default=1)

Other optional properties:

net-scale-factor(Default=1), network-mode(Default=0 i.e FP32),

model-color-format(Default=0 i.e. RGB) model-engine-file, labelfile-path,

mean-file, gie-unique-id(Default=0), offsets, process-mode (Default=1 i.e. primary),

custom-lib-path, network-mode(Default=0 i.e FP32)

The values in the config file are overridden by values set through GObject

properties.

[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
model-engine-file=/opt/nvidia/deepstream/deepstream-5.1/sources/deepstream_python_apps/models/tlt_pretrained_models/trafficcamnet/resnet18_trafficcamnet_pruned.etlt_b1_gpu0_fp16.engine
labelfile-path=/opt/nvidia/deepstream/deepstream-5.1/sources/deepstream_python_apps/models/tlt_pretrained_models/trafficcamnet/labels.txt
int8-calib-file=/opt/nvidia/deepstream/deepstream-5.1/sources/deepstream_python_apps/models/tlt_pretrained_models/trafficcamnet/cal_trt.bin
input-dims=3;544;960;0
uff-input-blob-name=input_1
batch-size=30
process-mode=1
model-color-format=0

0=FP32, 1=INT8, 2=FP16 mode

network-mode=1
num-detected-classes=4
interval=0
gie-unique-id=1
output-blob-names=conv2d_bbox;conv2d_cov/Sigmoid
force-implicit-batch-dim=1
#parse-bbox-func-name=NvDsInferParseCustomResnet
#custom-lib-path=/path/to/libnvdsparsebbox.so

0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)

cluster-mode=3
#scaling-filter=0
#scaling-compute-hw=0

[class-attrs-all]
pre-cluster-threshold=0.1
group-threshold=1

Set eps=0.7 and minBoxes for cluster-mode=1(DBSCAN)

eps=0.7
minBoxes=3
roi-top-offset=0
roi-bottom-offset=0
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0

Per class configurations

[class-attrs-0]
pre-cluster-threshold=0.15
eps=0.5
dbscan-min-score=0.45

[class-attrs-1]
pre-cluster-threshold=0.01
eps=0.5
dbscan-min-score=0.50

[class-attrs-2]
pre-cluster-threshold=0.3

Set eps=0.7 and minBoxes for cluster-mode=1(DBSCAN)

eps=0.25
dbscan-min-score=0.90

regards
gaurang patel

  1. The TAO TrafficCamNet has been updated for several versions. You may try the newer version to get better performence.
  2. The cluster parameters Gst-nvinfer — DeepStream documentation 6.4 documentation can be adjusted. You may try to set “threshold=0”, “pre-cluster-threshold=0” to keep the bboxes as much as possible.

Please give me link of Tao newer model

It’s give me false wrong direction detection for nvanalytics in deepstram python code.
I used above configure for detection

Regards
GaurangKumar patel

The TAO trafficcamnet is published in TrafficCamNet | NVIDIA NGC

Can you provide the video and your configurations(including nvanalytics configuration) you got the wrong direction?

[application]

enable-perf-measurement=1
perf-measurement-interval-sec=1

[property]
enable=1
enable_perf_measurement=1
config-width=1280
config-height=960
osd-mode=0
display-font-size=15

[roi-filtering-stream-0]
enable=1
roi-RF1=576;126;645;125;577;928;121;928
roi-RF2=659;171;705;101;1189;887;650;907
class-id=-1
inverse-roi=0

[direction-detection-stream-0]
enable=1
direction-FORWARD=436;658;571;241
direction-REVERSE=712;197;853;618
Wrong_direction_RF1=REVERSE
Wrong_direction_RF2=FORWARD
class-id=-1

Please find configuration for zone

Please suggest me model and how configure new model for above image seen. That new model.
Method to deploy with existing code.

Regards
GaurangKumar patel

There is already sample nvinfer configuration file in the NGC link. TrafficCamNet | NVIDIA NGC

I also require Truck class in that model. Please tell me major difference. From previous version.

Regards
GaurangKumar patel

For the model itself, please consult in TAO forum. Latest Intelligent Video Analytics/TAO Toolkit topics - NVIDIA Developer Forums

Please add my topic on Tao forums.

Regards
GaurangKumar patel

Topic closed as model related discssion in TAO already Trafficcamnet add truck class detection with other class and false detection occur.issue to detect small vehicles.miss detection.