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
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"
"Spatial net prototxt"
"Temporal net model"
"Temporal net prototxt"
Here, a matlab demo code for TDD extraction is released.
- Step 1: Improved Trajectory Extraction
You need download our modified iDT feature code and compile it by yourself. Improved Trajectories - Step 2: TVL1 Optical Flow Extraction
You need download our dense flow code and compile it by yourself. Dense Flow - Step 3: Mat Caffe
You need download the public caffe toolbox. Our TDD code is compatatible with previous version of Caffe - Step 4: TDD Extraction
Now you can run the matlab file "script_demo.m" to extract TDD features.
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