Object Tracking with YOLOv9 and DeepSORT on Jetson Nano

Autonomous Machines Jetson & Embedded Systems Jetson Projects
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I. Device Information

  • Main Device: Jetson Nano Developer Kit eMMC
  • CPU: Quad-core ARM A57 @ 1.43 GHz
  • GPU: 128-core Maxwell
  • RAM: 4 GB 64-bit LPDDR4 25.6 GB/s
  • Storage: eMMC (16GB onboard storage) + 64GB SD card
  • Camera: Logitech HD 720p
  • Operating System: Ubuntu 18.04 LTS (JetPack SDK)
  • PyTorch Version: v1.8.0
  • Python Version: 3.6.9

II. Steps to Set Up Jetson Nano

  • Create SwapFile
sudo fallocate -l 4G /var/swapfile 
sudo chmod 600 /var/swapfile
sudo mkswap /var/swapfile
sudo swapon /var/swapfile
sudo bash -c 'echo "/var/swapfile swap swap defaults 0 0"  >> /etc/fstab’
  • Install OpenCV 4.5
cd ~
wget -O opencv.zip https://github.com/opencv/opencv/archive/4.5.1.zip 
wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.5.1.zip 
unzip opencv.zip 
unzip opencv_contrib.zip

# Now rename the directories. Type each command below, one after the other.
mv opencv-4.5.1 opencv
mv opencv_contrib-4.5.1 opencv_contrib
rm opencv.zip
rm opencv_contrib.zip

# build OpenCV:
cd ~/opencv
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules -D EIGEN_INCLUDE_PATH=/usr/include/eigen3 -D WITH_OPENCL=OFF -D WITH_CUDA=ON -D CUDA_ARCH_BIN=5.3 -D CUDA_ARCH_PTX="" -D WITH_CUDNN=ON -D WITH_CUBLAS=ON -D ENABLE_FAST_MATH=ON -D CUDA_FAST_MATH=ON -D OPENCV_DNN_CUDA=ON -D ENABLE_NEON=ON -D WITH_QT=OFF -D WITH_OPENMP=ON -D WITH_OPENGL=ON -D BUILD_TIFF=ON -D WITH_FFMPEG=ON -D WITH_GSTREAMER=ON -D WITH_TBB=ON -D BUILD_TBB=ON -D BUILD_TESTS=OFF -D WITH_EIGEN=ON -D WITH_V4L=ON -D WITH_LIBV4L=ON -D OPENCV_ENABLE_NONFREE=ON -D INSTALL_C_EXAMPLES=OFF -D INSTALL_PYTHON_EXAMPLES=OFF -D BUILD_NEW_PYTHON_SUPPORT=ON -D BUILD_opencv_python3=TRUE -D OPENCV_GENERATE_PKGCONFIG=ON -D BUILD_EXAMPLES=OFF ..
make -j4 #This command below will take a long time (around 2 hours)

# torch v1.8.0
wget https://nvidia.box.com/shared/static/p57jwntv436lfrd78inwl7iml6p13fzh.whl -O torch-1.8.0-cp36-cp36m-linux_aarch64.whl
sudo apt-get install python3-pip libopenblas-base libopenmpi-dev libomp-dev
pip3 install 'Cython<3'
pip3 install numpy torch-1.8.0-cp36-cp36m-linux_aarch64.whl

# torchvision v0.9.0
sudo apt-get install libjpeg-dev zlib1g-dev libpython3-dev libopenblas-dev libavcodec-dev libavformat-dev libswscale-dev
git clone --branch v0.9.0 https://github.com/pytorch/vision torchvision
cd torchvision
export BUILD_VERSION=0.9.0  
python3 setup.py install --user
cd ../
pip install 'pillow<7'
  • Install albumentations to keep opencv version
pip install albumentations --no-deps
  • Monitoring software for Jetson Nano
cd ~
sudo -H pip3 install -U jetson-stats 
sudo reboot
jtop

jtop_image
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III. Steps to run Code

  • Clone the repository
git clone https://github.com/quanvovan/object-tracking-yolov9-deepSort.git
  • Goto the cloned folder.
cd Object-Tracking
  • Install requirements with mentioned command below.
pip install -r requirements.txt

  • Download the pre-trained YOLOv9 model weights
    yolov9

  • Run code

#for detection and tracking
python detect_dual_tracking.py --weights 'yolov9-c.pt' --source 'your video.mp4' --device 0

#for WebCam
python detect_dual_tracking.py --weights 'yolov9-c.pt' --source 0 --device 0

#for External Camera
python detect_dual_tracking.py --weights 'yolov9-c.pt' --source 1 --device 0

#For LiveStream (Ip Stream URL Format i.e "rtsp://username:pass@ipaddress:portno/video/video.amp")
python detect_dual_tracking.py --weights 'yolov9-c.pt' --source "your IP Camera Stream URL" --device 0

#for specific class (person)
python detect_dual_tracking.py --weights 'yolov9-c.pt' --source 'your video.mp4' --device 0 --classes 0

#for detection and tracking with trails 
!python detect_dual_tracking.py --weights 'yolov9-c.pt' --source 'your video.mp4' --device 0 --draw-trails
  • Output file will be created in the working-dir/runs/detect/obj-tracking with original filename

IV. Results

image
** Will update the video record

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Thanks for sharing!