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

• ALGLIB

  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.