Action Recognition

This is the implementation of 3-dimensional convolutional networks (C3D) approach for Action Recognition in Tensorflow. It contains complete code for preprocessing, training and test. Besides, this repository is easy-to-use and can be developed on Linux and Windows.

C3D :Tran, Du, et al. "Learning spatiotemporal features with 3d convolutional networks." Proceedings of the IEEE international conference on computer vision. 2015.

Getting Started

1 Prerequisites

• Python 3.x
• Tensorflow 1.x
• Opencv-python
• Pandas

2 Download this repo and unzip it

cd ../C3D/Label_Map
Open the label.txt and revise its class names as yours.

3 Generate directory

cd ../C3D/Code
run python make_dir.py
Then some subfolders will be generated in ../C3D/Raw_Data , ../C3D/Data/Train, ../C3D/Data/Test, ../C3D/Data/Val, where name of the subfolders is your class names defined in label.txt.

4 Prepare video clips

According to the class, copy your raw AVI videos to subfolders in ../C3D/Raw_Data. Optionally, you can use the public HMDB-51 dataset, which can be found here.
cd ../C3D/Code
run python prepare_clips.py
Clips generated will be saved in the subfolders in ../C3D/Data/Train, ../C3D/Data/Test, ../C3D/Data/Val. These clips will be used for training, test and validation.

5 Compute the mean image from training clips(optional)

cd ../C3D/Code
run python mean_img.py
And then a mean image is saved in directory ../C3D/Data/Train.

6 Train model

The model parameters, training parameters and eval parameters are all defined by parameters.py.
cd ../C3D/Code
run python train.py PB or python train.py CHECKPOINT
The model will be saved in directory ../C3D/Model, where "PB" and "CHECKPOINT" is two ways used for saving model for Tensorflow.

7 Test model(pb)

Test model using clips in ../C3D/Data/Test.
cd ../C3D/Code
run python test.py N
Where N is not more than the number of clips in test set. Note that we do not use batch during test. There may be out of memory errors with a large N. In this case, you can modify the test.py to use batch.

8 Visualize model using Tensorboard

cd ../C3D
run tensorboard --logdir=Model/
Open the URL in browser to visualize model.

Other Implementations

tensorflow-VTN

使用方法

1、安装环境依赖项

① Python3.6
② Tensorflow
③ Opencv-python
④ Pandas

2、下载这个工程到任意目录并解压

① 切换到目录 ../C3D/Label_Map,打开label.txt,将其中已有的类名修改为你的类名和对应的id。

3、创建保存数据的目录

① 切换到目录 ../C3D/Code,然后运行：python make_dir.py，在目录../C3D/Raw_Data 、../C3D/Data/Train、 ../C3D/Data/Test、../C3D/Data/Val 中将会创建子文件夹，文件夹名字为你的类名。

4、准备数据，生成视频片段（clips）

① 根据类别名，将你自己收集到的原始视频数据（AVI格式）复制到目录 ../C3D/Raw_Data 中对应的文件夹中。
② 切换到目录 ../C3D/Code, 然后运行：python prepare_clips.py，每个类生成的视频片段将会保存在 ../C3D/Data/Train, ../C3D/Data/Test, ../C3D/Data/Val 的子文件夹中，将被用于模型训练、评估和测试。

5、计算训练集的均值图像（可选的）

① 切换到目录 ../C3D/Code,然后运行：python mean_img.py，生成的均值图像将会保存在../C3D/Data/Train 目录下。
注：训练时，视频片段中每一帧图像将会被移除均值图像（原论文中并没有这一步预处理）。

6、训练模型

在 parameters.py中，你可以修改模型参数、训练参数、评估参数，以及生成训练数据的一些参数。
① 切换到目录 ../C3D/Code，然后运行python train.py PB 或者 python train.py CHECKPOINT,参数 "PB" 和 "CHECKPOINT"分别对应Tensorflow保存模型的两种方式。模型保存在 ../C3D/Model中。

7、测试模型（使用PB模型）

使用在 ../C3D/Data/Test 中的视频片段测试模型。
② 切换到目录 ../C3D/Code，然后运行python test.py N,这里N为小于等于测试集中clip的数量的正整数。
注：由于在测试集上测试时，并没有把测试集划分成多个batch来测试，如果一次性把测试集读入内存，内存可能不够。此时需要进一步修改test.py来实现批量测试。

8、Tensorboard 可视化模型

① 切换到目录 ../C3D/，执行：tensorboard --logdir=Model/，然后将显示的链接复制到浏览器中打开，可查看模型结构。

其他版本

tensorflow-VTN