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selflein / GraphNN-Multi-Object-Tracking

Unofficial PyTorch implementation of "Learning a Neural Solver for Multiple Object Tracking"
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MOT (Multi Object Tracking) using Graph Neural Networks

This repository largely implements the approach described in Learning a Neural Solver for Multiple Object Tracking. This implementation achieves ~58% MOTA on the MOT16 test set.

Note that this is not the official implementation of the paper which will be published here.

Setup

Install the conda environment

conda create -f environment.yml

Install torchreid

pip install git+https://github.com/KaiyangZhou/deep-person-reid.git

Train

The implementation supports the MOT16 dataset for training and testing.

Preprocessing

Run python src/data_utils/preprocessing.py which creates and saves a graph representation for the scene. In detail, the sequences are split into subsets with one overlapping frame each.

usage: preprocessing.py [-h] [--output_dir OUTPUT_DIR]
                        [--dataset_path DATASET_PATH] [--mode MODE]
                        [--threshold THRESHOLD] [--save_imgs]
                        [--device {cuda,cpu}]

optional arguments:
  -h, --help            show this help message and exit
  --output_dir OUTPUT_DIR
                        Outout directory for the preprocessed sequences
  --dataset_path DATASET_PATH
                        Path to the root directory of MOT dataset
  --mode MODE           Use train or test sequences (for test additional work
                        necessary)
  --threshold THRESHOLD
                        Visibility threshold for detection to be considered a
                        node
  --save_imgs           Save image crops according to bounding boxes for
                        training the CNN (only required if this is wanted)
  --device {cuda,cpu}   Device to run the preprocessing on.

--mode should be set to train.

Training script

Training accepts the preprocessed version of the dataset only.

usage: train.py [-h] --name NAME --dataset_path DATASET_PATH
                [--log_dir LOG_DIR] [--base_lr BASE_LR] [--cuda]
                [--workers WORKERS] [--batch_size BATCH_SIZE]
                [--epochs EPOCHS] [--train_cnn] [--use_focal]

optional arguments:
  -h, --help            show this help message and exit
  --name NAME           Name of experiment for logging
  --dataset_path DATASET_PATH   Directory of preprocessed data
  --log_dir LOG_DIR     Directoy where to store checkpoints and logging output
  --base_lr BASE_LR
  --cuda
  --workers WORKERS
  --batch_size BATCH_SIZE
  --epochs EPOCHS
  --train_cnn           Choose to train the CNN providing node embeddings
  --use_focal           Use focal loss instead of BCE loss for edge classification

Testing

(Optional) Obtain detections

If you do not want to use the public detections that come with the MOT16 dataset, run `src/data_utils/run_obj_detect.py` to use a pre-trained FasterRCNN for detection on the sequences. The FasterRCNN model weights can be downloaded [here](https://drive.google.com/file/d/12FlTPh5gjPqvY2u0N5Wxn089Hb1gFUb5/view?usp=sharing). ``` usage: run_obj_detect.py [-h] [--model_path MODEL_PATH] [--dataset_path DATASET_PATH] [--device DEVICE] [--out_path OUT_PATH] Run object detection on MOT16 sequences and generate output files with detections for each sequence in the same format as the `gt.txt` files of the training sequences optional arguments: -h, --help show this help message and exit --model_path MODEL_PATH Path to the FasterRCNN model --dataset_path DATASET_PATH Path to the split of MOT16 to run detection on. --device DEVICE --out_path OUT_PATH Output directory of the .txt files with detections ``` The output files can then easily be copied to the respective sequence folder, e.g., as `MOT16-02/gt/gt.txt` for the produced `MOT16-02.txt` file. In this way, we can just use the same pre-processing script from the training script.

Preprocessing

See Train section. Use with --mode test to use the test folder of the MOT16 dataset.

Inference

Run src/data_utils/inference.py to obtain tracks as .txt file for preprocessed sequences. Pretrained model weights can be downloaded from releases section.

usage: inference.py [-h] [--preprocessed_sequences PREPROCESSED_SEQUENCE_1 ...]
                    [--net_weights NET_WEIGHTS] [--out OUT]

optional arguments:
  -h, --help            show this help message and exit
  --preprocessed_sequences PREPROCESSED_SEQUENCE
                        Path to the preprocessed sequence (!) folders
  --net_weights NET_WEIGHTS
                        Path to the trained GraphNN
  --out OUT             Path of the directory where to write output files of
                        the tracks in the MOT16 format

Acknowledgements