Fisher Vector implementation for Dense Trajectory features
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Fisher Vector implementation for Dense Trajectories (DTFV)

This is a C++ implementation of Fisher Vector (FV) for Dense Trajectory (DT) features.

DTFV provides a binary with a Python script to generate Fisher Vectors. It also provides code to sample feature points, and to train PCA and GMM codebooks.

Some highlights include:

  • Provides a full pipeline from DT raw feature to Fisher Vectors
  • Aggregate feature points on-the-fly, no need to load all points in memory
  • Raw features are piped to the Fisher Vector generator, no need to store the large raw features
  • State-of-the-art performance for large action/event video datasets

It depends on:

  • Dense Trajectories (DT)
    To extract DT features
  • VLFeat
    To train Gaussian Mixture Models
    To train PCA projection matrix

Using linear SVM, we tested the code on two action/event classification datasets:

  • TRECVID Multimedia Event Detection (MED)
    Trained on EX100, tested on MEDTest. The mean average precision for event 6 to 15 and 21 to 30 is 0.33.
  • UCF 101
    Partitions were defined by THUMOS workshop. The average classification accuracy is 85%.

The pretrained codebooks on these two dataset are also provided.

Please see our WACV 2013 paper for more details.