Discriminative Hierarchical Rank Pooling for Activity Recognition

Basura Fernando, Peter Anderson, Marcus Hutter, Stephen Gould; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1924-1932

Abstract


We present hierarchical rank pooling, a video sequence encoding method for activity recognition. It consists of a network of rank pooling functions which captures the dynamics of rich convolutional neural network features within a video sequence. By stacking non-linear feature functions and rank pooling over one another, we obtain a high capacity dynamic encoding mechanism, which is used for action recognition. We present a method for jointly learning the video representation and activity classifier parameters. Our method obtains state-of-the art results on three important activity recognition benchmarks: 76.7% on Hollywood2, 66.9% on HMDB51 and, 91.4% on UCF101.

Related Material


[pdf]
[bibtex]
@InProceedings{Fernando_2016_CVPR,
author = {Fernando, Basura and Anderson, Peter and Hutter, Marcus and Gould, Stephen},
title = {Discriminative Hierarchical Rank Pooling for Activity Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}