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Learning a Neural Solver for Multiple Object Tracking

This the official implementation of our CVPR 2020 (oral) paper Learning a Neural Solver for Multiple Object Tracking (Guillem Brasó, Laura Leal-Taixe)
[Paper][Youtube][CVPR Daily] Method Visualization

Updates

  • (November 2020) Added support for MOT20 (including Tracktor object detector fine-tuning) and processing long sequences, solved issues with OOM errors.
  • (June 2020) Code release.

Setup

  1. Clone and enter this repository:

    git clone --recursive https://github.com/dvl-tum/mot_neural_solver.git 
cd mot_neural_solver

  2. Create an Anaconda environment for this project:

    1. conda env create -f environment.yaml
    2. conda activate mot_neural_solver
    3. pip install -e tracking_wo_bnw
    4. pip install -e .

  3. (OPTIONAL) Modify the variables DATA_PATH, and OUTPUT_PATH in src/mot_neural_solver/path_cfg.py so that they are set to your preferred locations for storing datasets and output results, respectively. By default, these paths will be in this project's root under folders named data and output, respectively.

  4. Download the MOTChallenge data by running:

    bash scripts/setup/download_motcha.sh

  5. Download the our reid network, Tracktor's object detector, and our trained models:

    bash scripts/setup/download_models.sh

  6. (OPTIONAL) For convenience, we provide the preprocessed detection files. You can download them by running:

    bash scripts/setup/download_prepr_dets.sh

  7. (NEW) If you are going to be working with MOT20, run the following to download the dataset, preprocessed detections, and pretrained models:

    bash scripts/setup/download_mot20.sh

Running Experiments

We use Sacred to configure our experiments, and Pytorch Lightning, to structure our training code. We recommend reading these libraries' documentations for an overview.

You can configure training and evaluation experiments by modifying the options in configs/tracking_cfg.yaml. As for preprocessing, all available options can be found in configs/preprocessing_cfg.yaml. Note that you can also use Sacred's command line interface to modify configuration entries. We show some examples in the sections below.

For every training/evaluation experiment you can specify a run_id string. This, together with the execution date will be used to create an identifier for the experiment being run. A folder named after this identifier, containing model checkpoints, logs and output files will be created at $OUTPUT_PATH/experiments(OUTPUT_PATH is specified at src/mot_neural_solver/path_cfg.py).

Preprocessing Detections

NOTE: You can skip this step if you will only be working with the MOT15, MO16, MOT17and MOT20 datasets, and run steps 6 and 7 of Setup.

As explained in the paper, we preprocess public detections by either running Tracktor (with no ReID) on them (1) or filtering false positives and refining box coordinates with a pretrained object detector (2).

On the MOT15, MO16 and MOT17 you can run the first preprocessing scheme (1) with:

python scripts/preprocess_detects.py

To run (1) on the MOT20 dataset, run instead:

python scripts/preprocess_detects.py with configs/mot20/preprocessing_cfg.yaml

If you want to use the alternative scheme (2), run the following:

python scripts/preprocess_detects.py with prepr_w_tracktor=False

All these scripts will store the preprocessed detections in the right locations within $DATA_PATH.

If you use the second option (2), make sure to set add the named configuration configs/no_tracktor_cfg.yaml to your training and evaluation experiments by adding with configs/no_tracktor_cfg.yaml after your python command.

Fine-Tuning Tracktor

In order to obtain results for MOT20, we fine-tuned Tracktor on it. To do so, we borrowed all code from this Colab Notebook, which was made public in Tracktor's repository, and organized it under obj_detect and a python script. You can reproduce the fine-tuning of the model we provide in step 7 of Setup by running:

python scripts/train_obj_detect.py

As a sanity check, we made a submission to the MOT20 test dataset with this model, and obtained the following results. For reference, we include the comparison with the results made public by Tracktor's authors on the CVPR19 Tracking Challenge, and our MOT Neural Solver built on top of the fine-tuned Tracktor.

Dataset Method MOTA IDF1 MT ML
CVPR19 Challenge Tracktor++ 51.3 47.6 313 (24.9%) 326 (26.0%)
MOT20 Tracktor with no ReID, fine-tuned by us 52.1 44.0 362(29.1%) 332 (26.7%)
MOT20 Ours (MPNTrack) 57.6 59.1 474 (38.2%) 279 (22.5%)

Training

You can train a model by running:

python scripts/train.py

By default, sequences MOT17-04 and MOT17-11 will be used for validation, and all remaining sequences in the MOT15 and MOT17 datasets will be used for training. You can use other validation sets by modifying the parameters data_splits.train and data_splits.val, or use several splits and perform cross-validation.

In order to train with all available sequences, and reproduce the training of the MOT17 model we provide, run the following:

python scripts/train.py with data_splits.train=all_train train_params.save_every_epoch=True train_params.num_epochs=6

For training a model on the MOT20 dataset, you need to use its named configuration configs/mot20/tracking_cfg.yaml. For instance, to reproduce the training of the MOT20 model we provide run the following:

python scripts/train.py with configs/mot20/tracking_cfg.yaml train_params.save_every_epoch=True train_params.num_epochs=22

NOTE: The first time you use a sequence for training or testing, it will need to be processed. This means that ground truth boxes (if available) will be assigned to detection boxes, detection files will be stored with sequence metainformation, and (possibly) reid embeddings will be computed and stored. This process should take ~30 mins for train/test sets of MOT15 and MOT17 and only needs to be performed once per set of detections. Computing reid embeddings in advance is optional for testing but required for training. Doing so, speeds up training significantly and reduces substantially the training memory requirements. As explained in our paper, we observed no significant performance boost from training CNN layers.

The reid network was trained with torchreid, by using ResNet50's default configuration with images resized to 128 x 56, adding two fully connected layers (see resnet50_fc256 in src/mot_neural_solver/models/resnet.py) and training for 232 epochs. The training script will be provided in a future release.

Evaluation

You can evaluate a trained model on a set of sequences by running:

python scripts/evaluate.py

The weights used and sequences tested are determined by parameters ckpt_path and data_splits.test, respectively. By default, the weights from the model we provide will be used and the MOT15 and MOT17 test sequences will be evaluated. The resulting output files yield the following MOT17 metrics on the train/test set:

MOT17 MOTA IDF1 FP FN IDs MT ML
Train 64.4 70.8 5087 114460 504 636 (38.8%) 362 (22.1%)
Test 58.4 62.1 17836 214869 1146 655 (27.8%) 793 (33.7%)

Note that these results show a slight difference with respect to the ones reported in the paper. Specifically, IDF1 has improved by 0.5 points, and MOTA has decreased by 0.4 points. This change is due to using a newer pytorch version and small code differences introduced while cleaning-up.

In order to evaluate a model on the MOT20 dataset, run the following:

python scripts/evaluate.py with configs/mot20/tracking_cfg.py

The resulting output files yield the following MOT20 train and test performance

MOT20 MOTA IDF1 FP FN IDs MT ML
Train 70.1 66.9 38260 299110 1821 1073 (48.4%) 362 (10.8%)
Test 57.6 59.1 16953 201384 1210 474 (38.2%) 279 (22.5%)

Cross-Validation

As explained in the paper, we perform cross-validation to report the metrics of ablation experiments. To do so, we divide MOT17 sequences in 3 sets of train/val splits. For every configuration, we then run 3 trainings, one per validation split, and report the overall metrics.

You can train and evaluate models in this manner by running:

RUN_ID=your_config_name
python scripts/train.py with run_id=$RUN_ID cross_val_split=1
python scripts/train.py with run_id=$RUN_ID cross_val_split=2
python scripts/train.py with run_id=$RUN_ID cross_val_split=3
python scripts/cross_validation.py with run_id=$RUN_ID

By setting cross_val_split to 1, 2 or 3, the training and validation sequences corresponding to the splits we used in the paper will be set automatically (see src/mot_neural_solver/data/splits.py).

The last script will gather the stored metrics from each training run, and compute overall MOT17 metrics with them. This will be done by searching output files containing $RUN_ID on them, so it's important that this tag is unique.

MOTA IDF1 FP FN IDs MT ML
Cross-Val 64.3 70.5 5610 114284 531 643 (39.3%) 363 (22.2%)

Citation

If you use our work in your research, please cite our publication:

    @InProceedings{braso_2020_CVPR,
    author={Guillem Brasó and Laura Leal-Taixé},
    title={Learning a Neural Solver for Multiple Object Tracking},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2020}
}

Please, also consider citing Tracktor if you use it for preprocessing detections:

  @InProceedings{tracktor_2019_ICCV,
  author = {Bergmann, Philipp and Meinhardt, Tim and Leal{-}Taix{\'{e}}, Laura},
  title = {Tracking Without Bells and Whistles},
  booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
  month = {October},
  year = {2019}}