YOLOv8 + Pose estimation+ ByteTracker

This project integrates YOLOv8 (Object detection and pose estimation)and ByteTracker for real-time, TensorRT-optimized object detection and tracking, extending the existing TensorRT-YOLOv8 implementation.

Usage CPP(TensorRT):

• 1.Clone yolov8 and download yolov8s-pose.pt
• 2.Export onnx model

from ultralytics import YOLO
# Load a model
model = YOLO("yolov8s-pose.pt")  # load an official model
# Export the model
model.export(format="onnx")

• 3.Convert model to trt

trtexec.exe --onnx=yolov8s-pose.onnx --saveEngine=yolov8s-pose.engine

• 4.Inference

cd build/release
yolov8-pose-bytetrack-trt.exe yolov8s-pose.engine test.mp4 # the video path

What is next?

• Python(TensorRT)

Setup:Build project by using the following commands or cmake-gui(Windows).

CPP:

1. Windows:

mkdir build
cd build
cmake ..
cmake --build . --config Release

2. Linux(not tested):

mkdir build
cd build && mkdir out_dir
cmake ..
make

Requirement

• TensorRT
• CUDA, CudaNN
• Eigen 3.3
• C++ compiler with C++17 or higher support
• CMake 3.14 or higher
• OpenCV

Acknowledgement

This project is based on the following awesome projects:

• YOLOv8 - YOLOv8: Ultralytics.
• ByteTrack - C++ implementation of ByteTrack algorithm.
• yolov9-bytetrack-tensorrt - C++ implementation of YOLOv9 and bytetrack using TensorRT API.