Follow-Me Project

Real-time Multiple Object Tracking with Yolov4, Tensorflow and Deep SORT.

1.0 Installation

It is necessary to install some tools before installation.

1.1 Anaconda Installation

The recommendation is to create a variable environment using Anaconda.

• You can install in: https://docs.anaconda.com/anaconda/install/index.html

1.1.1 Anaconda for Jetson Nano

• Link 01: https://sahilramani.com/2020/10/how-to-setup-python3-and-jupyter-notebook-on-jetson-nano/
• Link 02: https://docs.anaconda.com/anaconda/install/linux-aarch64/

1.2 Nvidia Installation (only GPU Mode)

If you to use GPU mode, you need to install Nvidia Driver and CUDA.

1.2.1 GPU Mode Installation

• Install GPU NVIDIA Driver: https://www.nvidia.com/Download/index.aspx
• Install CUDA v10.1: https://developer.nvidia.com/cuda-10.1-download-archive-update2

1.2.2 CPU Mode Installation

• It is not necessary

2.0 Getting Started

You can run the project in GPU and CPU mode. Follow the instructions below.

2.1 Download this Project with Git Clone or Download

2.1.1 Git Clone

git clone https://github.com/eduardocarnunes/follow_me_03

2.1.2 Download

2.2 Install libraries for GPU or CPU Mode

2.2.1 Mode CPU

conda env create -f conda-cpu.yml
conda activate follow-me-cpu

2.2.2 Mode GPU

conda env create -f conda-gpu.yml
conda activate follow-me-gpu

2.3 Pre-processing

We need convert YOLO to Tensorflow

Step 1: Create a folder with name: weights

Step 2: Download and save weights files into weights folder

• Yolov4: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT

Step 3: Convert darknet weights to tensorflow model. Execute convert.py in a terminal:

• Yolov4

python save_model.py --model yolov4

2.4 Run Multiple Object Tracking

Insert params in config.yml

# True: Camera Real Time 
# False: Video File
is_mode_camera : True 
id_camera : 0

# True: to salve result in .avi 
# False: don't to save in .avi
is_save_result_video : True 

# FPS video result file
fps_save_result : 15

# resolution of camera 
# make_1080p: 1920x1080
# make_720p: 1280x720
# make_480p: 640x480
# make_custom: width x height (set in )
set_resolution : make_1080p
# uses when make_custom
resolution_custom_width : 640
resolution_custom_height : 480

# Input Size Yolo. Must be multiple of 32
input_size_yolo : 640

# If is_mode_camera == False, put video path .mp4
video_path_test : ./data/video/test2.mp4

# If is_save_result_video == True, put video path .avi
video_path_result : ./data/result/result_test2.avi

# True: show preview result 
is_preview_result : True
# True: info (id, coords, class name) show 
is_show_result_cmd : False
# True: show result topside on preview
is_show_count_object : True

# True: Save all result in a .csv file
is_save_result_csv : False
# If save_info_result == True, path file where .csv will save
path_file_csv : ./data/result.csv

# allowe classes Yolov4 for detection
allowed_classes : 
  - person
  - car

Run detection

python multiple_object_tracker.py

Some Results

Mode Video File with GPU:

Mode Video File with CPU:

Mode WebCam Real Time with GPU:

Result info in .csv

License list of libraries and tools used for this project:

• Anaconda (Python distribution) : Free use and redistribution under the terms of the EULA for Anaconda Individual Edition.
• matplotlib : https://matplotlib.org/stable/users/project/license.html
• OpenCV : https://opencv.org/license/
• cudnn : https://docs.nvidia.com/deeplearning/cudnn/sla/index.html#license
• cudatoolkit : https://docs.nvidia.com/cuda/eula/index.html#abstract
• tensorflow-gpu : https://www.tensorflow.org/install/gpu
• lxml : https://lxml.de/index.html#license
• tqdm : https://github.com/tqdm/tqdm/blob/master/LICENCE
• absl-py : https://github.com/abseil/abseil-py/blob/main/LICENSE
• easydict : https://github.com/makinacorpus/easydict/blob/master/LICENSE
• pillow : https://pillow.readthedocs.io/en/stable/about.html#license
• PyYAML : https://pypi.org/project/PyYAML/
• pandas : https://pandas.pydata.org/pandas-docs/stable/getting_started/overview.html#license-

References

• https://www.youtube.com/watch?v=zi-62z-3c4U