# pedestrians-tracking

**Repository Path**: Vinncent/pedestrians-tracking

## Basic Information

- **Project Name**: pedestrians-tracking
- **Description**: Pedestrians detection and tracking using Pytorch.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-20
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Pedestrians identification and tracking - Class project - June 2020
### Students : André Gomes, Yann Martinson, Sylvain Pietropaolo

This final project (Milestone 3) combines two previous projects where models where trained for pedestrian detection (Milestone 1) and re-identification (Milestone 2). The objective is here to take a video as input and output the same video with labelled bounding boxes on the different pedestrians tracked.

## Credits
Milestone 1 code is mainly based on an existing Pytorch tutorial available under: https://colab.research.google.com/github/pytorch/vision/blob/temp-tutorial/tutorials/torchvision_finetuning_instance_segmentation.ipynb.

Milestone 2 code is adapted from the work of Zhedong Zheng: https://github.com/layumi/Person_reID_baseline_pytorch

For further details, please read the associated reports. 

## Examples
Three examples of the work done by the proposed algorithm are found in folder Final_videos. 
![Demo](demo/demo.gif)

## Installation
The libraries in requirements.txt are needed to properly execute the code. 

## GPU or not
The code for Milestone 3 is written in order to run without GPU. To accelerate the execution using GPUs, the code can easily be adapted.

## Get trained models
Identification: the trained model (resnet50_ped) on ECP dataset can be found here:

https://drive.google.com/file/d/1xofm9jQtkgiIuO3XynwKVB1fbuVsXpPg/view?usp=sharing

Re-identification: the trained model (PCB.pth) on the MSMT17 dataset can be found here:

https://drive.google.com/file/d/1eERK2rQ5E84i_thb-rhfkNY-7rX0zsxe/view?usp=sharing

Both model files should be saved inside the 'Models' folder.

## Execution
To execute the code, run the following command:

	python3 run.py --path_to_video <path_to_video> --path_to_stack <path_to_stack>  --path_to_save <path_to_save> --show True

options:

	--path_to_video 	default='./Videos/MOT16-10-raw.webm'		=> path to the input video
	--path_to_stack 	default='./images/new_frames/video1'	 	=> path to the output images stack
	--path_to_save	 	default='./Final_videos/final_video9.avi'	=> path to the output video
	--show		 	default=False					=> show each frame after processing