Using OpenCV to capture video from camera or video file, then use YOLOv8 TensorRT to detect objects and DeepSORT TensorRT or BYTETrack to track objects.

Support for both NVIDIA dGPU and Jetson devices.

All project YOLOv8_Object_Tracking_TensorRT

Demo

OpenCV + YOLOv8 + BYTETrack on NVIDA Geforce GTX 1660Ti

Performance

Both OpenCV YOLOv8 and DeepSORT TensorRT

Using OpenCV to capture video from camera or video file, then use YOLOv8 TensorRT to detect objects and DeepSORT TensorRT to track objects.

YOLOv8 TensorRT model

Test speed of YOLOv8 TensorRT model using trtexec from TensorRT

/usr/src/tensorrt/bin/trtexec on NVIDIA Jetson

batch size = 1

DeepSORT TensorRT model

Test speed of DeepSORT TensorRT model using trtexec from TensorRT

/usr/src/tensorrt/bin/trtexec on NVIDIA Jetson

batch size = 1

For NVIDIA dGPU

Environment

• NVIDIA CUDA: 11.4
• NVIDIA TensorRT: 8.5.2

Clone repository

Clone repository and submodules

git clone --recurse-submodules https://github.com/nabang1010/YOLOv8_DeepSORT_TensorRT.git

Prepare enviroment

Create new enviroment

conda create -n yolov8_ds python=3.8

Activate enviroment

conda activate yolov8_ds

Prepare models

Go to refs/YOLOv8-TensorRT and install requirements for exporting models

cd refs/YOLOv8-TensorRT
pip3 install -r requirements.txt
pip3 install tensorrt easydict pycuda lap cython_bbox

Install python3-libnvinfer

sudo apt-get install python3-libnvinfer

Download YOLOv8 weights from ultralytics here: yolov8n.pt and save in folder models/to_export

Export YOLOv8 ONNX model

In refs/YOLOv8-TensorRT run the following command to export YOLOv8 ONNX model

python3 export-det.py \
--weights ../../models/to_export/yolov8n.pt \
--iou-thres 0.65 \
--conf-thres 0.25 \
--topk 100 \
--opset 11 \
--sim \
--input-shape 1 3 640 640 \
--device cuda:0

The output .onnx model will be saved in models/to_export folder, move the model to models/onnx folder

mv ../../models/to_export/yolov8n.onnx ../../models/onnx/yolov8n.onnx

Export YOLOv8 TensorRT model

In refs/YOLOv8-TensorRT run the following command to export YOLOv8 TensorRT model

python3 build.py \
--weights ../../models/onnx/yolov8n.onnx \
--iou-thres 0.65 \
--conf-thres 0.25 \
--topk 100 \
--fp16  \
--device cuda:0

The output .engine model will be saved in models/onnx folder, move the model to models/trt folder

mv ../../models/onnx/yolov8n.engine ../../models/engine/yolov8n.engine

Build OpenCV

bash build_opencv.sh

Export DeepSORT TensorRT model (if use BYTETrack, ignore this step)

Install libeigen3-dev

apt-get install libeigen3-dev

Go to refs/deepsort_tensorrt and run the following command to build onnx2engine

cd refs/deepsort_tensorrt
mkdir build
cd build
cmake ..
make -j$(nproc)

If catch error fatal error: Eigen/Core: No such file or directory, replace #include <Eigen/*> with #include <eigen3/Eigen/*> in all files of this repo (datatype.h, kalmanfilter.cpp) and rebuild again.

If catch error error: looser exception specification on overriding virtual function 'virtual void Logger::log(nvinfer1::ILogger::Severity add noexcept before override in logger.h line 239 and rebuild again.

Run following command to export DeepSORT TensorRT model

./build/onnx2engine ../../models/onnx/deepsort.onnx ../../models/engine/deepsort.engine

Run script

Go to src folder

cd src

Run YOLOv8 + DeepSORT

python3 yolov8_deepsort_trt.py --show

Run YOLOv8 + DeepSORT

python3 yolov8_bytetrack_trt.py --show

For NVIDIA Jetson Device

Coming soon

References

• ultralytics
• YOLOv8-TensorRT
• deepsort_tensorrt
• yolov5_deepsort_tensorrt
• ByteTrack

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