https://github.com/wanglimin/TDD

Trajectory-pooled Deep-Convolutional Descriptors
https://github.com/wanglimin/TDD

action-recognition caffe

Last synced: 18 days ago
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Trajectory-pooled Deep-Convolutional Descriptors

• Host: GitHub
• URL: https://github.com/wanglimin/TDD
• Owner: wanglimin
• Created: 2015-07-21T07:20:16.000Z (almost 9 years ago)
• Default Branch: cudnn2.0
• Last Pushed: 2017-08-24T19:33:41.000Z (almost 7 years ago)
• Last Synced: 2024-02-26T22:41:12.513Z (4 months ago)
• Topics: action-recognition, caffe
• Language: Matlab
• Size: 520 KB
• Stars: 102
• Watchers: 15
• Forks: 75
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Lists

• Awesome-Caffe - TDD

README

        
# Trajectory-Pooled Deep-Convolutional Descriptors

Here we provide the code for the extraction of Trajectory-Pooled Deep-Convolutional Descriptors (TDD), from the following paper:

    Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors

    Limin Wang, Yu Qiao, and Xiaou Tang, in CVPR, 2015

### Updates

- Dec 24, 2015

  * Release the second version of TDD (branch: cudnn2.0) compatible with latest [caffe toolbox](https://github.com/yjxiong/caffe). Due to speedup brought by cudnn2.0 or above, TDD extraction is becoming more efficient.

- Jul 21, 2015

  * Release the first version TDD (branch: master) compatible with an older version of [caffe toolbox](https://github.com/wanglimin/caffe).

### Two-stream CNN models trained on the UCF101 dataset

First, we provide our trained two-stream CNN models on the split1 of UCF101 dataset, which achieve the recognition accuracy of 84.7%

["Spatial net model (v1)"](http://mmlab.siat.ac.cn/tdd/spatial.caffemodel)  

["Spatial net prototxt (v1)"](http://mmlab.siat.ac.cn/tdd/spatial_cls.prototxt) 

["Temporal net model (v1)"](http://mmlab.siat.ac.cn/tdd/temporal.caffemodel) 

["Temporal net prototxt (v1)"](http://mmlab.siat.ac.cn/tdd/temporal_cls.prototxt)

### TDD demo code

Here, a matlab demo code for TDD extraction is provided.

- **Step 1**: Improved Trajectory Extraction 

You need download our modified iDT feature code and compile it by yourself. [Improved Trajectories](https://github.com/wanglimin/improved_trajectory)

- **Step 2**: TVL1 Optical Flow Extraction 

You need download our dense flow code and compile it by yourself. [Dense Flow](https://github.com/wanglimin/dense_flow)

- **Step 3**: Matcaffe  

You need download the public caffe toolbox. Our TDD code is compatatible with the latest version of [parallel caffe toolbox](https://github.com/yjxiong/caffe). 

**Note that you need to download the models in the new proto format:** 

["Spatial net model (v2)"](http://mmlab.siat.ac.cn/tdd/spatial_v2.caffemodel) ["Temporal net model (v2)"](http://mmlab.siat.ac.cn/tdd/temporal_v2.caffemodel) 

- **Step 4**: TDD Extraction 

Now you can run the matlab file "script_demo.m" to extract TDD features.

### Questions

Contact 

- [Limin Wang](http://wanglimin.github.io/)