Tracking code for the winner of track 1 in the MMP-Tracking Challenge at ICCV 2021 Workshop.

DamoCV

Last update: Nov 13, 2022

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Deep Learning MMP_Track1_ICCV21

Overview

Tracking Code for the winner of track1 in MMP-Trakcing challenge

This repository contains our tracking code for the Multi-camera Multiple People Tracking (MMP-Tracking) Challenge at ICCV 2021 Workshop.

1. Environment setup

This tracking code has been tested on Python 3.7.6, Pytorch 1.5.1, CUDA 10.1, please install related libraries before running this code:

pip install -r requirements.txt
pip install torch==1.5.1+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html

2. Detection

We provide the detection results on the test split in yolox_baseline.zip and yolox_finetune.zip. They should be extracted first.

3. Tracking

Run the following script to reproduce our tracking results.

sh auto_run.sh

4. Acknowledgement

We use YOLOX to train our detection model. Some code from deep_sort, FairMOT, and tao.

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Comments

Regarding test data detections used in the repo.

Hi, I am doing Research for my Master's thesis on multi camera multi object tracking. I have seen your presentation, that was really nice. I was going through your code. You have provided the detections for the test data. How did you got the test data.

I have mailed the ICCV2021 team, and I got the dataset link for train and validation set but not the test set that you used.

I am actually hoping that whether you can provide some link to the test data or some refence for that so that I can understand your code in a better way. This would help a lot in my thesis project.

Thanks.

opened by Umar1998 0

Owner

DamoCV

CV team of DAMO academy

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Tracking code for the winner of track 1 in the MMP-Tracking Challenge at ICCV 2021 Workshop.

Related tags

Overview

Tracking Code for the winner of track1 in MMP-Trakcing challenge

1. Environment setup

2. Detection

3. Tracking

4. Acknowledgement

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