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Slicing Convolutional Neural Network for Crowd Video Understanding

This is the source code for "Slicing Convolutional Neural Network for Crowd Video Understanding". It aims at learning generic spatio-temporal features from crowd videos, especially for long-term temporal learning (i.e. 100 frames).

Overview

Three-branch Slicing CNN model (i.e. xy-, xt-, and yt-branch)

Crowd attribute recognition (i.e. 94 crowd-related attributes)

Project Site

Caffe

A fork of the well-known Caffe framework with Multi-GPU training and Dimension Swap layer.

Apart from the official installation prerequisites, we have several other dependencies:

1. Install openmpi to allow multi-gpu running
2. Python packages (e.g. numpy, scipy, scikit-image, etc.)
3. Add export PYTHONPATH="[path_python_layer]:$PYTHONPATH" to ~/.bashrc and restart the terminal. Here [path_python_layer] indicates the absolute path of the python script of py_dim_swap_layer.py.

Get the Caffe code

git clone --recursive https://github.com/amandajshao/Slicing-CNN.git

Files

• Dataset

  The dataset is introduced in CVPR 2015 which contains 10,000 crowd videos from 8,257 different crowded scenes with annotated 94 attributes.

• LMDB Data

  The LMDB data used in the model with training/validation/test splits.

• CNN Initial Model

  The initial model (VGG-16) is pre-trained on UCF-101 action dataset (single frame) and fine-tuned on WWW dataset (single frame).

  Dropbox link

  BaiduDisk link: http://pan.baidu.com/s/1jH5VLNw (password: 76zl)

• CNN Best Model

  Three models: SCNN-xy, SCNN-xt, SCNN-yt.

  Dropbox link

  BaiduDisk link: http://pan.baidu.com/s/1pK7h5sJ (password: j024)

• Prototxt

  The prototxts are corresponding to the above three models (SCNN-xy/-xt/-yt).

  Dropbox link

  BaiduDisk link: http://pan.baidu.com/s/1o85xUI2 (password: mwvo)

• Scripts

  There are two scripts provided in our code: model_run.sh and extract_features.sh.

Related Projects

Deeply Learned Attributes for Crowd Scene Understanding

Thanks

• Kai Kang
• Tong Xiao
• Yuanjun Xiong

Citation

J. Shao, C. C. Loy, K. Kang, and X. Wang. Slicing Convolutional Neural Network for Crowd Video Understanding. Computer Vision and Pattern Recognition (CVPR), 2016.

@article{shao2016scnn,
  title={Slicing Convolutional Neural Network for Crowd Video Understanding},
  author={Shao, Jing and Loy, Chen Change and Kang, Kai and Wang, Xiaogang},
  booktitle={Computer Vision and Pattern Recognition (CVPR)},
  year={2016}
}