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STEP: Spatio-Temporal Progressive Learning for Video Action Detection, CVPR 2019 (Oral)
Xitong Yang, Xiaodong Yang, Ming-Yu Liu, Fanyi Xiao, Larry Davis, Jan Kautz

STEP is a fully end-to-end action detector that performs detection simply from a handful of initial proposals with no need of relying on an extra person detector.

• Getting Started
  • Installation
  • (Optional) Demo
• Training on AVA Dataset
  • Dataset Preparation
  • Testing
  • Training
  • Tips
• Citation
• Related Work
• License

• Prerequisites: Python 3.6, NumPy, OpenCV
• Install PyTorch (>= 1.1.0) and torchvision (>= 0.2.1)
• (Optional) You may skip this. Install APEX for half-precision training:

git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext

• Clone this repo:

git clone https://github.com/NVlabs/STEP.git
cd STEP/

• Install external packages (for RoI pooling/align and NMS):

python setup.py build develop

Try STEP on your own video data! Our model pre-trained on the AVA dataset can effectively detect common actions (e.g., stand, sit, walk, run, talk to, etc.) in general videos.

First, extract frames of your own videos and organize them in datasets/demo/frames/ as follows:

|-- frames/
|   |-- <video_id1>/
|       |-- frame0000.jpg
|       |-- frame0001.jpg
|       |-- ...
|   |-- <video_id2>/
|   |-- ...

Second, modify the file demo.py:

• checkpoint_path: the path to the trained STEP model. You can use the model you trained on your own (see Training), or our trained model downloaded from Google Drive and Baidu Disk.
• args.data_root: the path to your video frames, and the default is datasets/demo/frames/
• source_fps: frame rate of your own videos
• (optional) conf_thresh and global_thresh: thresholds for confidence scores and global NMS, these are the values you can control for better visualization

Finally, run the script for action detection:

python demo.py

The detection results and visualization will be saved in datasets/demo/results/ by default.

Download AVA. Note that our code uses the version AVA v2.1.

Put all the annotation-related files into the folder datasets/ava/label/. Transform the origional annotation files in csv format to pickle files:

python scripts/generate_label.py <path_to_train_csv>
python scripts/generate_label.py <path_to_val_csv>

Extract frames from the downloaded videos and store them in datasets/ava/frames/. You can check out the code scripts/extract_clips.py for the process (ffmpeg is required).

The extracted frames are organized as follows:

|-- frames/
|   |-- <video_id>/
|       |-- <timestamp>/ 
|           |-- <frame_id>

Each folder <timestamp>/ contains the frames within a 1-second interval, starting from that timestamp (for example, the first frame 00000.jpg in the folder 01000/ corresponds to the frame exactly at timstamp 1000). This organization is made for precise alignment with the AVA annotations (in other words, the annotation at a certein timstamp corresponds to the first frame in the folder of that timestamp). As the annoations are provided at timestamps 902:1798 inclusive, we can safely extract the frames at timestamps only from 900 to 1800.

You can save your dataset and annotations in other directories. If so, you need to modify the default pathes in the training scripts, as mentioned in the next section.

We provide our trained models to reproduce the results reported in our paper. You can download the weights from Google Drive or Baidu Disk, and put it in pretrained/.

Run the following command for testing and evaluation on the validation set of AVA:

python test.py

The output will be stored in datasets/ava/cache/STEP-max3-i3d-two_branch/.

STEP achieves 20.2% mAP on AVA v2.1 using this implementation (updated in arxiv).

As the classification task on the AVA dataset is challenging, we perform classification pre-training on AVA using the ground truth annotations before training the detection models. Our classification pre-trained weights (mAP = 26.4%) can be downloaded from Google Drive and Baidu Disk, and we put it in pretrained/.

Now we are ready to train STEP, using the following script:

cd scripts
bash train_step.sh

Note that you need to modify data_root, save_root and pretrain_path if you save them in the other places.

You can train STEP with the low precision (fp16), by add a flag --fp16 at the end of the script file scripts/train_step.sh (APEX is required for fp16 training).

You can also train your own pre-trained model using the following script:

cd scripts
bash train_cls.sh

If so, you need the kinetics-pretrained weights for the I3D network, which can be downloaded from Google Drive and Baidu Disk and then put in pretrained/.

GPU memory requirement for the default setting (3 steps, 34 initial proposals, batch size 8):

• fp32, 4GPUs: >= 15G
• fp16, 4GPUs: >= 10G

Please cite this paper if it helps your research:

@inproceedings{cvpr2019step,
   title={STEP: Spatio-Temporal Progressive Learning for Video Action Detection},
   author={Yang, Xitong and Yang, Xiaodong and Liu, Ming-Yu and Xiao, Fanyi and Davis, Larry S and Kautz, Jan},
   booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
   year={2019}
}

In the folder external/, we modify the code from ActivityNet for parsing annotation files and evaluation, and the code from maskrcnn-benchmark for RoI pooling/align and NMS. Please follow the corresponding license to use the code.

Copyright (C) 2019 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International). The code is released for academic research use only. For commercial use, please contact [email protected].