LPC_MOT

This is the code for the paper "Learning a Proposal Classifier for Multiple Target tracking"

Setup

1. Clone the enter this repository:

git clone https://github.com/daip13/LPC_MOT.git

2. Create a docker image for this project:

   • Python = 3.7.7
   • PyTorch = 1.4.0+cu100
   • Notice: We also provide the docker image (password: lq3v) to run our codes.

3. Copy the LPC_MOT repository to the root path of the docker image.

4. Download our GCN and reid network.

   • The models can also be downloaded here (password: lq3v).
   • You should place the models to path /root/LPC_MOT/models/
   • Notice: we adopt the fast-reid as our reid model. However, the authors have updated their codes. In order to get the same reid features with our trained model, we also present the codes that we used here.

5. (OPTIONAL) For convenience, we provide the detections files with extracted reid features. You can also download them here (password: lq3v).

   • You should place the downloaded data to /root/LPC_MOT/dataset/

6. Running.

cd /root/LPC_MOT/learnable_proposal_classifier/scripts/
bash main.sh ../../dataset/MOT17/results_reid_with_traindata/detection/ ../../models/dsgcn_model_iter_30.pth /tmp/LPC_MHT/ ../../dataset/MOT17/results_reid_with_traindata/tracking_output/ ../../dataset/MOT17/train/

GCN Model Training

The scripts for GCN model training will be here soon.