MiviaLab/gesture-recognition

Benchmarking Deep Neural Networks for Gesture Recognition on Embedded Devices

Implementation of the framework used in Benchmarking Deep Neural Networks for Gesture Recognition on Embedded Devices. The workflow is developed to perform a benchmarking focusing not only on the accuracy but also on the computational burden, involving two different architectures (2D and 3D), with two different backbones (MobileNet, ResNeXt) and four types of input modalities (RGB, Depth, Optical Flow, Motion History Image) and their combinations. The system performs gesture recognition following the following scheme.

Modalities can refer to data acquired directly by sensors (RGB or Depth stream) or by pre-processing raw data so as to obtain for instance OF or MHI. Each modality feeds a deep learning based feature extractor, which takes into account both spatial and temporal information. Finally, the classification is performed over the different feature vectors extracted and a fusion at decision level is performed. The dashed edges of a module refers to the fact that it is not mandatory.

Results

The results analyze are analyzed on ChaLearn LAP IsoGD and NVIDIA Dynamic Gesture dataset, and are accuracy of the classification, memory occupancy and inference time of the model.

The following tables show the experimental results in terms of \textit{percentage accuracy}. The analyzed modalities are: RGB, Depth (D), dense Optical Flow (OF), Motion History Image (MHI), and their combinations. Tested networks are 2D and 3D versions of MobileNet v2 and ResNeXt-101.

ChaLearn LAP IsoGD

NVIDIA Dynamic Gesture

Computational Cost

Memory requirements and time analysis for feature extraction and classification steps. The numbered columns, from 1 to 4, indicate the number of streams of the multi-stream network (one for each modality).

Datasets

The framework can work on any dataset, but the dataloaders have been implemented only for the most interesting datasets, namely: 20BN-Jester, ChaLearn LAP IsoGD, and NVIDIA Dynamic Gesture. Such dataloaders expect to find datasets in their respective formats presented below.

20BN-Jester

|- RGB_frames
|--- 1
|----- 00000.jpg
|----- ...
|----- n_frames
|--- ...

ChaLearn LAP IsoGD

|- MHI_frames
|--- test
|----- 001
|------- K_00000
|--------- image_00000.jpg
|--------- ...
|--------- n_frames
|------- ...
|------- M_00000
|--------- ...
|--- ...
|--- train
|----- ...
|--- valid
|----- ...
|- OF_frames
|--- ...
|- RGB-D_frames
|--- ...

NVIDIA Dynamic Gesture

|- MHI_frames
|---- class_01
|------ subject10_r0
|-------- sk_color
|---------- image_00001.jpg
|---------- ...
|---------- n_frames
|-------- sk_dept
|------...
|- OF_frames
|--- ...
|- RGB-D_frames
|--- ...

Requirements

The main requirements are include in the following list.

av==8.0.1
einops==0.3.2
fvcore==0.1.5
matplotlib==3.4.2
numpy==1.19.5
opencv_python==4.5.2.54
pandas==1.3.0
Pillow==9.0.0
psutil==5.8.0
pytorch_model_summary==0.1.2
scikit_image==0.18.2
scikit_learn==1.0.2
scipy==1.7.1
setuptools==57.1.0
simplejson==3.17.6
skimage==0.0
slowfast==1.0
submitit==1.4.1
timm==0.4.12
torch==1.9.0
torchinfo==1.5.2
torchsummary==1.5.1
torchvision==0.10.0
tqdm==4.61.2
visdom==0.1.8.9

Pre-training

All the results reported are obtained starting from model pretrained on the 20BN-Jester dataset. The pre-trained models can be downloaded from here. The implemented models are:

• 2D - MobileNet V2
• 2D - ResNeXt-101
• 3D - MobileNet V2
• 3D - ResNeXt-101

The command for pre-train the system is the following.

python3 main.py \
  --root_path ./ \
  --video_path ../datasets/ \
  --annotation_path annotation_Jester/jester.json \
  --result_path results \
  --dataset jester \
  --n_classes 27 \
  --n_finetune_classes 27 \
  --ft_portion complete \
  --model resnext \
  --n_epochs 60 \
  --lr_steps 30 45 \
  --learning_rate 0.01 \
  --batch_size 32 \
  --n_threads 8 \
  --no_hflip \
  --modalities RGB \
  --mod_aggr none \
  --gpu 0

Training

The training phase can be done with the following command. It contains the main required parameters.

python3 main.py \
  --root_path ./ \
  --video_path ../datasets/ \
  --annotation_path annotation_ChaLearn_IsoGD/chalearn_isogd.json \
  --result_path results \
  --pretrain_path pretrained_models/jester_resnext_101_RGB_16_best.pth \
  --dataset isogd \
  --n_classes 27 \
  --n_finetune_classes 249 \
  --ft_portion complete \
  --model resnext \
  --n_epochs 20 \
  --lr_steps 8 15 \
  --learning_rate 0.1 \
  --batch_size 32 \
  --n_threads 8 \
  --no_hflip \
  --modalities RGB \
  --mod_aggr none \
  --gpu 0

Testing

The following command load a specific model for test the system.

python3 main.py \
  --root_path ./ \
  --video_path ../datasets/ \
  --annotation_path annotation_ChaLearn_IsoGD/chalearn_isogd.json \
  --result_path results \
  --pretrain_path results \
  --dataset isogd \
  --n_classes 249 \
  --n_finetune_classes 249 \
  --model resnext \
  --batch_size 16 \
  --n_threads 4 \
  --no_train \
  --no_val \
  --test \
  --test_subset test \
  --modalities RGB \
  --mod_aggr none \
  --preds_per_video 249 \
  --gpu 0

Citation

Please cite the following paper if you feel this repository useful.

@inproceedings{bini2022benchmarking,
  title={Benchmarking deep neural networks for gesture recognition on embedded devices},
  author={Bini, Stefano and Greco, Antonio and Saggese, Alessia and Vento, Mario},
  booktitle={2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
  pages={1285--1290},
  year={2022},
  organization={IEEE}
}