/video-cnn-feat

Primary LanguagePython

Extracting CNN features from video frames by MXNet

The video-cnn-feat toolbox provides python code and scripts for extracting CNN features from video frames by pre-trained MXNet models. We have used this toolbox for our winning solution at TRECVID 2018 ad-hoc video search (AVS) task and in our W2VV++ paper.

Requirements

Environments

  • Ubuntu 16.04
  • CUDA 9.0
  • python 2.7
  • opencv-python
  • mxnet-cu90
  • numpy

We used virtualenv to setup a deep learning workspace that supports MXNet. Run the following script to install the required packages.

virtualenv --system-site-packages ~/cnn_feat
source ~/cnn_feat/bin/activate
pip install -r requirements.txt
deactivate

MXNet models

1. ResNet-152 from the MXNet model zoo

# Download resnet-152 model pre-trained on imagenet-11k
./do_download_resnet152_11k.sh

# Download resnet-152 model pre-trained on imagenet-1k
./do_download_resnet152_1k.sh

2. ResNeXt-101 from MediaMill, University of Amsterdam

Send a request to xirong ATrucDOTeduDOTcn for the model link. Please read the ImageNet Shuffle paper for technical details.

Get started

Our code assumes the following data organization. We provide the toydata folder as an example.

collection_name
+ VideoData
+ ImageData
+ id.imagepath.txt

The toydata folder is assumed to be placed at $HOME/VisualSearch/. Video files are stored in the VideoData folder. Frame files are in the ImageDatafolder.

  • Video filenames shall end with .mp4, .avi, .webm, or .gif.
  • Frame filenames shall end with .jpg.

Feature extraction for a given video collection is performed in the following four steps. Skip the first step if frames are already there.

Step 1. Extract frames from videos

collection=toydata
./do_extract_frames.sh $collection

Step 2. Extract frame-level CNN features

./do_resnet152-11k.sh $collection
./do_resnet152-1k.sh $collection
./do_resnext101.sh $collection

Step 3. Obtain video-level CNN features (by mean pooling over frames)

./do_feature_pooling.sh $collection pyresnet-152_imagenet11k,flatten0_output,os
./do_feature_pooling.sh $collection pyresnet-152_imagenet1k,flatten0_output,os
./do_feature_pooling.sh $collection pyresnext-101_rbps13k,flatten0_output,os

Step 4. Feature concatenation

featname=pyresnext-101_rbps13k,flatten0_output,os+pyresnet-152_imagenet11k,flatten0_output,os
./do_concat_features.sh $collection $featname

Acknowledgements

This project was supported by the National Natural Science Foundation of China (No. 61672523).

References

If you find the package useful, please consider citing our MM'19 paper:

@inproceedings{li2019w2vv++,
title={W2VV++: Fully Deep Learning for Ad-hoc Video Search},
author={Li, Xirong and Xu, Chaoxi and Yang, Gang and Chen, Zhineng and Dong, Jianfeng},
booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
pages={1786--1794},
year={2019}
}