CrossMOT

CrossMOT

Framework

Installation

Dependencies

• Python 3.8
• PyTorch >= 1.7.0

Environment Setup

conda create -n CrossMOT python=3.8
conda activate CrossMOT
conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=10.2 -c pytorch
cd ${CrossMOT_ROOT}
pip install cython
pip install -r requirements.txt

DCNv2 Backbone Network

We use DCNv2_pytorch_1.7 in our backbone network (pytorch_1.7 branch). Previous versions can be found in DCNv2.

git clone -b pytorch_1.7 https://github.com/ifzhang/DCNv2.git
cd DCNv2
./make.sh

Data preparation

Datasets We use five cross-view MOT datasets:

• EPFL
• DIVOTrack
• MvMHAT, CAMPUS
• CAMPUS
• Wildtrack

Visit their official websites to download the images and annotations. After downloading the datasets, please put all the images in {CrossMOT_root}/dataset/dataset_name/images. Make sure the name of each image follows the format {scene_name}_{view_name}_{frame_id}.jpg. Then, use the annotation file and {CrossMOT_root}/src/dataset_util/ to generate the corresponding training file {dataset_name}.train and store it in {CrossMOT_root}/src/data/.

Directory Structure

Before training, the dataset should have the following structure:

${dataset_name}
   ├── images
   │   └── train
   └── labels_with_ids
       ├── train
       └── test

Pretrained model

We use the prertained model of FairMOT The models can be downloaded here: fairmot_dla34.pth [Google] [Baidu, code:uouv] [Onedrive].

After downloading, place the baseline model in the following structure:

${CrossMOT_ROOT}
   └── models
       └── fairmot_dla34.pth

Training

1. Download the training data.
2. Change the dataset root directory root in src/lib/cfg/data.json and data_dir in src/lib/opts.py.
3. Run the appropriate command for the dataset you want to train:

Train DIVOTrack

sh ./experiments/train_divo.sh

Train MvMHAT

sh ./experiments/train_mvmhat.sh

Train Campus

sh ./experiments/train_campus.sh

Tracking

For tracking, you only need to specify the tracking model by running the following command:

cd src
python track.py mot --load_model {your path to the tracking model} --test_divo --conf_thres 0.5 --reid_dim 512 --track_name {your exp name}

You can download our model from this link.

Ablation Study

You can modify the training or tracking settings to reproduce our ablation study. The instructions are as follows:

Training Options

• --baseline: Set this to 1 for the shared embedding experiment. You need to further specify --baseline_view: 0 for single-view embedding, 1 for cross-view embedding.
• --single_view_id_split_loss: Use this to achieve single-view conflict-free loss.
• --cross_view_id_split_loss: Use this to achieve cross-view conflict-free loss.
• --reid_dim: Set the dimension of single-view and cross-view embeddings.
• --zero_start: Set this to if the person ID in the dataset start with zero.

Tracking Options

• --conf_thres: Set the detection threshold. Default is 0.5.
• --single_view_threshold: Set the single-view association threshold. Default is 0.3.
• --cross_view_threshold: Set the cross-view association threshold. Default is 0.5.