Action Recognition for Self-Driving Cars
This repo contains the codes for the 2021 Fall semester project “Action Recognition for Self-Driving Cars” at EPFL VITA lab.
For experiment results, please refer to the project report and presenation slides at docs. A demo video is available here.
This project utilizes a simple yet effective architecture (called poseact) to classify multiple actions.
The model has been tested on three datasets, TCG, TITAN and CASR.
Preparation and Installation
This project mainly depends PyTorch. If you wish to start from extracting poses from images, you would also need OpenPifPaf (along with posetrack plugin), please also refer to this section for following steps. In case you wish to skip extracting your own poses, and directly start from the poses used in this repo, you can download this folder. It contains the poses extracted from TITAN and CASR dataset as well as a trained model for TITAN dataset. For the poses in TCG dataset, please refer to the official repo.
First, clone and install this repo. If you have downloaded the folder above, please put the contents to poseact/out/
Then clone this repo and install in editable mode.
git clone https://github.com/vita-epfl/pose-action-recognition.git
cd Action_Recognition
python -m pip install -e .
Project Structure and usage
poseact
|___ data # create this folder to store your datasets, or create a symlink
|___ models
|___ test # debug tests, may also be helpful for basic usage
|___ tools # preprocessing and analyzing tools, usage stated in the scripts
|___ utils # utility functions, such as datasets, losses and metrics
|___ xxxx_train.py # training scripts for TCG, TITAN and CASR
|___ python_wrapper.sh # script for submitting jobs to EPFL IZAR cluster, same for debug.sh
|___ predictor.py # a visualization tool with the model trained on TITAN dataset
It’s advised to cd poseact and conda activate pytorch before running the experiments.
To submit jobs to EPFL IZAR cluster (or similar clusters managed by slurm), you can use the script python_wrapper.sh. Just think of it as “the python on the cluster”. To submit to debug node of IZAR, you can use the debug.sh
Here is an example to train a model on TITAN dataset. --imbalance focal means using the focal loss, --gamma 0 sets the gamma value of focal loss to 0 (because I find 0 is better :=), --merge_cls means selecting a suitable set of actions from the original actions hierarchy, and--relative_kp means using relative coordinates of the keypoints, see the presentation slides for intuition. You can specify a name for this task with --task_name, which will be used to name the saved model if you use --save_model.
sbatch python_wrapper.sh titan_train.py --imbalance focal --gamma 0 --merge_cls --relative_kp --task_name Relative_KP --save_model
To use the temporal model, you can use --model_type sequence, and maybe you will need to adjust the number of epochs, batch size and learning rate. To use pifpaf track ID instead of ground truth track ID, you can use --track_method pifpaf .
sbatch python_wrapper.sh titan_train.py --model_type sequence --num_epoch 100 --imbalance focal --track_method gt --batch_size 128 --gamma 0 --lr 0.001
For all available training options, please refer to the comments and docstrings in the training scripts.
All the datasets have “train-validate-test” setup, so after the training, you should be able to see a summary of evaluation.
Here is an example
In general, overall accuracy 0.8614 avg Jaccard 0.6069 avg F1 0.7409
For valid_action actions accuracy 0.8614 Jaccard score 0.6069 f1 score 0.9192 mAP 0.7911
Precision for each class: [0.885 0.697 0.72 0.715 0.87]
Recall for each class: [0.956 0.458 0.831 0.549 0.811]
F1 score for each class: [0.919 0.553 0.771 0.621 0.839]
Average Precision for each class is [0.9687, 0.6455, 0.8122, 0.6459, 0.883]
Confusion matrix (elements in a row share the same true label, those in the same columns share predicted):
The corresponding classes are {'walking': 0, 'standing': 1, 'sitting': 2, 'bending': 3, 'biking': 4, 'motorcycling': 4}
[[31411 1172 19 142 120]
[ 3556 3092 12 45 41]
[ 12 1 157 0 19]
[ 231 160 3 512 26]
[ 268 9 27 17 1375]]
After training and saving the model (to out/trained/), you can use the predictor to visualize results on TITAN (all sequences). Feel free to change the chekpoint to your own trained model, but only the file name is needed, because models are assumed to be out/trained
sbatch python_wrapper.sh predictor.py --function titanseqs --save_dir out/recognition --ckpt TITAN_Relative_KP803217.pth
It’s also possible to run on a single sequence with --function titan_single --seq_idx <Number>
or run on a single image with --function image --image_path <path/to/your/image.png>
More about the TITAN dataset
For the TITAN dataset, we first extract poses from the images with OpenPifPaf, and then match the poses to groundtruth accoding to IOU of bounding boxes. After that, we store the poses sequence by sequence, frame by frame, person by person, and you will find corresponding classes in titan_dataset.py.
Preparing poses for TITAN and CASR
This part may be a bit cumbersome and it’s advised to use the prepared poses in this folder. If you want to extract the poses yourself, please also download that folder, because poseact/out/titan_clip/example.png is needed as the input to OpenPifPaf.
First, install OpenPifPaf and the posetrack plugin.
For TITAN, download the dataset to poseact/data/TITAN and then
cd poseact
conda activate pytorch # activate the python environment
# run single frame pose detection , wait for the program to complete
sbatch python_wrapper.sh tools/run_pifpaf_on_titan.py --mode single --n_process 6
# run pose tracking, required for temporal model with pifpaf track ID, wait for the program to complete
sbatch python_wrapper.sh tools/run_pifpaf_on_titan.py --mode track --n_process 6
# make the pickle file for single frame model
python utils/titan_dataset.py --function pickle --mode single
# make the pickle file from pifpaf posetrack result
python utils/titan_dataset.py --function pickle --mode track
For CASR, you should agree with the terms and conditions required by the authors of CASR
CASR dataset needs some preprocessing, please create the folder poseact/scratch (or link to the scratch on IZAR) and then
cd poseact
conda activate pytorch # activate the python environment
sbatch tools/casr_download.sh # wait for the whole process to complete, takes a long time
sbatch python_wrapper.sh tools/run_pifpaf_on_casr.py --n_process 6 # wait for this process to complete, again a long time
python ./utils/casr_dataset.py # now you should have the file out/CASR_pifpaf.pkl
Credits
The poses are extracted with OpenPifPaf.
The model is inspired by MonoLoco and the heuristics are from this work
The code for TCG dataset is adopted from the official repo.
