README.md

Video to Geospatial with Yolov5 + Deep Sort

Introduction

This tool tracks people on a video and convert them to a geospatial references.

This repository is based https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch and contains a moded version of PyTorch YOLOv5 (https://github.com/ultralytics/yolov5). It filters out every detection that is not a person. The detections of persons are then passed to a Deep Sort algorithm (https://github.com/ZQPei/deep_sort_pytorch) which tracks the persons. The reason behind the fact that it just tracks persons is that the deep association metric is trained on a person ONLY datatset.

It was developed as part of the Master in City and Technology - IAAC by:

• Adriana Aguirre Such
• Diana Roussi
• Dongxuan Zhu
• Hebah Qatanany
• Tugdual Sarazin

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

pip install -U -r requirements.txt

All dependencies are included in the associated docker images. Docker requirements are:

• nvidia-docker
• Nvidia Driver Version >= 440.44

Before you run the tracker

1. Clone the repository recursively:

git clone --recurse-submodules https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch.git

If you already cloned and forgot to use --recurse-submodules you can run git submodule update --init

2. Github block pushes of files larger than 100 MB (https://help.github.com/en/github/managing-large-files/conditions-for-large-files). Hence you need to download two different weights: the ones for yolo and the ones for deep sort

• download the yolov5 weight from the latest realease https://github.com/ultralytics/yolov5/releases. Place the downlaoded .pt file under yolov5/weights/
• download the deep sort weights from https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6. Place ckpt.t7 file underdeep_sort/deep/checkpoint/

Configuration

Configure the different files

The configuration is saved on the config.py file:

class Config:
    # Path of the video source file
    video_path = 'data/bcn_jaume/jaume_5_10am.MOV'
    # Path of the tracking video 
    video_save_path = 'data/bcn_jaume/jaume_5_10am_tracking.MOV'
    # Path of the reference points
    refpoints_geojson_path = 'data/bcn_jaume/jaume_5_10am_refpoints.geojson'
    # Path of the transposition matrices
    converter_path = 'data/bcn_jaume/jaume_5_10am_converter'
    # Path where video geospatial tracking is saved   
    history_geojson_path = 'data/bcn_jaume/jaume_5_10am_geohistory.geojson'
    # The frame frequency when the tracking is done
    frame_rate = 10
    # Yolo detection threshold 
    yolo_thres = 0.10

Initialize the geospatial transposition

Configuration required for this:

• Config.refpoints_geojson_path
• Config.video_path

This step configures the transposition (video to geospatial) parameters file. It is done with the configure_geo.py program. It requires 4 geospatial points file (Config.refpoints_geojson_path), this file must contain 4 geospatial points (EPSG:4326). The user interface of the configure_geo.py helps you to set the position of these 4 points on the video (Config.video_path). It will save the transposition parameters in the file Config.converter_path.

Run it :

python configure_geo.py

Tracking

The tracking program identifies people on the video (Config.video_path), follows them and assigns them an id. The program save the analysis video in the Config.video_save_path file . The position of people is convert to the geospatial (epsg:4326) and save in the file Config.history_geojson_path.

Run it :

python track.py

Other information

For more detailed information about the algorithms and their corresponding lisences used in this project access their official github implementations.