Efficient feature extraction, encoding and classification for action recognition

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Abstract

Local video features provide state-of-the-art performance for action recognition. While the accuracy of action recognition has been continuously improved over the recent years, the low speed of feature extraction and subsequent recognition prevents current methods from scaling up to real-size problems. We address this issue and first develop highly efficient video features using motion information in video compression. We next explore feature encoding by Fisher vectors and demonstrate accurate action recognition using fast linear classifiers. Our method improves the speed of video feature extraction, feature encoding and action classification by two orders of magnitude at the cost of minor reduction in recognition accuracy. We validate our approach and compare it to the state of the art on four recent action recognition datasets.

Paper

CVPR paper
CVPR poster

 
 Efficient feature extraction, encoding and classification for action recognition. Vadim Kantorov, Ivan Laptev In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2014. 

@inproceedings{kantorov2014,
  author = {Kantorov, V. and Laptev, I.},
  title = {Efficient feature extraction, encoding and classification for action recognition},
  booktitle = {Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2014},
  year = {2014},
}

Code

The code for MPEG flow-based descriptors and fast Fisher vector signatures is available on GitHub.

Datasets

Funding

This research project is supported by Quaero and MSR-INRIA.

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