Please provide complete information as applicable to your setup.
**• Hardware Platform (Jetson / GPU) XAVIER NX
**• DeepStream Version 5.0
**• JetPack Version (valid for Jetson only) 4.4
**• TensorRT Version 7.1
• NVIDIA GPU Driver Version (valid for GPU only)
**• Issue Type( questions, new requirements, bugs) bugs
I want to recognize human behavior. My steps are:
- Identify the location of people.
- Using openpose to detect the key points of human.
The system works well in real time, but it will block at a certain frame. And every run is blocked in this frame.like the following pic shows :
Running both models separately is normal, so the model and video files are normal. But when the two models are combined, there is a blocking situation.
I think the reason for the blocking is that the recognition speed of the first model is fast, while the recognition speed of the second model is slow. But I don’t know how to solve this problem. Does anyone have a similar problem? Can anyone help me?
I am modify the deepstream-test1 to complete my work. my config file as follows:
(1)dstest1_pgie_config.txt
[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
tlt-model-key=tlt_encode
tlt-encoded-model=…/…/…/…/samples/models/tlt_pretrained_models/peoplenet/resnet34_peoplenet_pruned.etlt
labelfile-path=labels_peoplenet.txt
model-engine-file=…/…/…/…/samples/models/tlt_pretrained_models/peoplenet/resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine
input-dims=3;544;960;0
uff-input-blob-name=input_1
#force-implicit-batch-dim=1
batch-size=1
process-mode=1
model-color-format=0
0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=3
cluster-mode=1
interval=0
gie-unique-id=1
output-blob-names=output_bbox/BiasAdd;output_cov/Sigmoid
[class-attrs-all]
pre-cluster-threshold=0.5
Set eps=0.7 and minBoxes for cluster-mode=1(DBSCAN)
eps=0.6
minBoxes=1
(2)dstest1_sgie_config.txt
[property]
gpu-id=0
#net-scale-factor=1
net-scale-factor=0.0174292
#net-scale-factor=0.0039215697906911373
offsets=123.675;116.28;103.53
model-file=pose_estimation.onnx
model-engine-file=pose.engine
force-implicit-batch-dim=1
batch-size=1
0=FP32 and 1=INT8 mode
network-mode=2
input-object-min-width=35
input-object-min-height=56
process-mode=2
model-color-format=1
gpu-id=0
gie-unique-id=2
operate-on-gie-id=1
operate-on-class-ids=0
#is-classifier=1
classifier-async-mode=1
classifier-threshold=0.3
output-tensor-meta=1
network-type=100
workspace-size=3000
#secondary-reinfer-interval=15