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SCUT-AILab/BPAI-Net
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This repo holds the codes and models for the BPAI-Net framework.
Bidirectional Posture-Appearance Interaction Network for Driver Behavior Recognition, Mingkui Tan*, Gengqin Ni*, Xu Liu, Shiliang Zhang, Xiangmiao Wu, Yaowei Wang†, Runhao Zeng†.
Install the runtime environment by running
conda env create -f environment.yml
Clone this repo with git
git clone https://github.com/SCUT-AILab/BPAI-Net
We support experimenting with two publicly available datasets for driver behavior recognition: Drive&Act and PCL-BDB. Here are some steps to download these two datasets.
Drive&Act: you can download it from theDrive&Act website. The skeleton data can be obtained from Baidu cloud (URL:https://pan.baidu.com/s/1Ia3OyVmNL0Ql6VWzIa6h8w password: on7x). When you download and unpack the dataset, you should configure the path of dataset in opts.py file, such as "--root", "--train_split" and so on.
PCL-BDB: We will release PCL-BDB dataset soon.
The recall scores of BPAI-Net with different backbone on Drive&Act.
| Model | Backbone | Recall |
|---|---|---|
| BPAI-Net | MobileNet V2 | 64.03 |
| BPAI-Net | ResNet50 | 65.34 |
| BPAI-Net | Inception V1 | 67.83 |
The recall scores of BPAI-Net with different backbone on PCL-BDB.
| Model | Backbone | Recall |
|---|---|---|
| BPAI-Net | MobileNet V2 | 85.92 |
| BPAI-Net | ResNet50 | 85.84 |
The BPAI-Net checkpoints with different backbone can be get fromhere.
Use the following commands to train BPAI-Net
#train BPAI-Net with ResNet50 backbone on Drive&Actpython main_drive.py --arch fusion --arch_cnn resnet50 --num_segments 8 --xyc --first layer2 --dropout 0.8 --shift --mode train --root_model exp/test --root_log exp/test --tune_from=pretrained/TSM_kinetics_RGB_resnet50_shift8_blockres_avg_segment8_e50.pth --gcn_pretrained=pretrained/st_gcn.kinetics.pt#train BPAI-Net with ResNet50 backbone on PCL-BDBpython main_drive.py --dataset pcl --arch fusion --arch_cnn resnet50 --num_class 40 --num_segments 8 --first layer2 --xyc --batch-size 8 --dropout 0.8 --shift --mode train --root_model exp/test --root_log exp/test --root dataset/pcl-bdb/ --skeleton_json dataset/pcl-bdb/video_pose --tune_from=pretrained/TSM_kinetics_RGB_resnet50_shift8_blockres_avg_segment8_e50.pth --gcn_pretrained=pretrained/st_gcn.kinetics.pt --pcl_anno annotation(2)(1).json
Use the following commands to test BPAI-Net
#test BPAI-Net with ResNet50 backbone on Drive&Actpython test_drive.py --arch fusion --arch_cnn resnet50 --num_segments 8 --xyc --first layer2 --shift --test_crops=1 --batch-size=8 --modetest --model_path tsm_new/exp/test/checkpoint.best.pth --root_log exp/test/#test BPAI-Net with ResNet50 backbone on PCL-BDB python test_drive.py --dataset pcl --arch fusion --arch_cnn resnet50 --num_segments 8 --num_class 40 --first layer2 --xyc --test_crops=1 --batch-size=8 --modetest --model_path exp/test/checkpoint.best.pth --root_log exp/test --pcl_anno annotation(2)(1).json --root dataset/pcl-bdb/ --skeleton_json dataset/pcl-bdb/video_pose
More train and test commands refer toscript.sh.
For any question, please file an issue or contact
Gengqin Ni: 394885025@qq.com or gengqinni@gmail.comRunhao Zeng: runhaozeng.cs@gmail.com