spatio-time-mtmc

Introduction

We release the code on AI City 2021 Challenge (https://www.aicitychallenge.org/) Track 3, AiForward - Team15. We get IDF1 score 0.5654.

Install

please note your cuda version and reference get-started while install pytorch.

conda create --name st-mtmc python==3.7
pip3 install torch torchvision torchaudio 
git clone https://github.com/facebookresearch/detectron2
cd detectron2
python setup.py build develop
pip install -e .
cd ..
git clone https://github.com/zxcver/spatio-time-mtmc.git
cd spatio-time-mtmc
pip install -r docs/requirement.txt

Data Preparation

If you want to reproduce our results on AI City Challenge , please download the data set from: (https://www.aicitychallenge.org/2021-data-and-evaluation/) and put it under the folder datasets. Make sure the data structure is like:

spatio-time-mtmc

• datasets
  • AIC21_Track3_MTMC_Tracking
    • cam_framenum
    • cam_timestamp
    • eval
    • train
    • cam_loc
    • test
    • validation

and transfer video to images in validation,test and train folders:

python transfer/video2images.py

Inference

we designed a separate pipeline to control each stage more intuitively, complate inference pipeline include detection,nms,expand,mot,filter and mtmc.

you can inference with ours pretrained model in best model:

cd spatio-time-mtmc
mkdir weights
cd weights
mkdir embedding

Then put the pretrained model under this folder and run:

sh script/allin/complete_inference.sh

besides, you also can inference some stage separately.

finally, you can get results in spatio-time-mtmc/resultpipeline/mtmc/S06

• selfzero visual result with mtmc
• selfzero.txt result doc for submission

Training

If you want to train the model by yourself, please first generate training sets through:

python transfer/prepare_dataset.py

and

python3 tools/train_net.py \
        --config-file ./configs/AICity/bagtricks_R101-ibn.yml --num-gpus 8 \
        TEST.IMS_PER_BATCH 256 SOLVER.MAX_EPOCH 120 SOLVER.IMS_PER_BATCH 256 \
        INPUT.SIZE_TRAIN [256,256] INPUT.SIZE_TEST [256,256] 

Reference

fast-reid

detectron2

FairMOT

ELECTRICITY-MTMC