source code for Finding Action Tubes, CVPR 2015
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README.md

Finding Action Tubes

Source code for Finding Action Tubes, created by Georgia Gkioxari at UC Berkeley.

Gitter

Contents

  1. Requirements
  2. Usage
  3. Instructions
  4. Downloads

Requirements

You need Caffe. Please refer to Caffe installation instructions

Usage

The pipeline consists of multiple steps. For simplicity, we break them down to independent procedures.

  1. In startup.m, add the paths and resolve the dependencies

  2. Selective search boxes need to be stored in the following format
    /ss_dir/motion/action/video/0000f.mat
    Video frames need to be stored in the following format
    /img_dir/action/video/0000f.png

  3. Optical flow computation

     compute_OF/compute_flow.m  
     	for a pair of images computes flow as described in the paper.  
     		im1, im2: input images  
     		optical flow images need to be stored in the format `/flow_dir/action/video/0000f.png`
    
  4. Motion saliency

     motion_saliency/get_motion_salient_boxes.m  
     	for each frame, prunes boxes (e.g. from Selective Search) based the optical flow signal within each box.  
     		 annot: set of videos and actions, jhmdb_annot.mat  
     		 ss_dir: directory containing the boxes  
     		 flow_dir: directory containing the optical flow images (as computed by A.)
    
  5. Extract fc7 features

     extract_features/rcnn_cache_fc7_features_jhmdb.m  
     	extracts fc7 features  
     		 type: 'spatial' or 'motion'  
     		 net_def_file: prototxts, `models/jhmdb/extract_fc7.prototxt`. Same for any type  
     		 net_file: models, use pretrained models for JHMDB as provided in the project page  
     		 output_dir: cache directory, the features are cached in output_dir/type/action/video/frame.mat
    
  6. Train SVM models

     train_svm/train_jhmdb.m  
     	trains SVM models, one for each action  
     		annot: ground truth information and boxes (after pruning), jhmdb_motion_sal_annot.mat  
     		feat_dir: directory with cached features  
     		save_dir: cache directory
    
  7. Action Tubes

     train_svm/compute_tubes.m  
     	scores and links detections to create the final action tubes  
     		annot: source of boxes, `jhmdb_motion_sal_annot.mat`  
     		rcnn_model: the models as computed by train_jhmdb.m
    
  8. Precomputed tubes

     test_tubes/  
     	tubes for all three splits of J-HMDB and UCF sports  
     test_tubes/UCFsports_benchmark/  
     	AUC and ROC numbers for UCFSports and plots (see ipython notebook)
    
  9. Evaluate/ROC curves

     evaluate/get_ROC_curve_JHMDB.m  
     	computes ROC and AUC for JHMDB  
     		annot: ground truth annotation (annot_jhdmb.mat)  
     		tubes: tubes on the test set  
     		actions: list of actions  
     		iou_thresh: threshold for intersection over union  
     		draw: true to draw the curves  
     (For UCF sports the same function was used with some small adjustments regarding the format of the data)
    

Instructions

To train action tubes from scratch, you need to train two networks.

Training spatial-CNN and motion-CNN

1. Compute the optical flow as in A.
2. Create `window_train(val).txt` with the window data (similar to R-CNN detection)
3. Use Caffe to train (train prototxt is given in `models/jhmdb/train.prototxt`), and initialize with the proper model
4. In the case of motion-CNN, you need to make two changes in `window_data_layer.cpp`
	a. The image mean is for all channels 128 (instead of the image mean provided)
	b. During training, when flipping of the input image occurs the flow in x needs to also change sign

Downloads

Download pretrained models from the project's webpage