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Second-order Temporal Pooling for Action Recognition

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Abstract

Deep learning models for video-based action recognition usually generate features for short clips (consisting of a few frames); such clip-level features are aggregated to video-level representations by computing statistics on these features. Typically zero-th (max) or the first-order (average) statistics are used. In this paper, we explore the benefits of using second-order statistics.Specifically, we propose a novel end-to-end learnable feature aggregation scheme, dubbed temporal correlation pooling that generates an action descriptor for a video sequence by capturing the similarities between the temporal evolution of clip-level CNN features computed across the video. Such a descriptor, while being computationally cheap, also naturally encodes the co-activations of multiple CNN features, thereby providing a richer characterization of actions than their first-order counterparts. We also propose higher-order extensions of this scheme by computing correlations after embedding the CNN features in a reproducing kernel Hilbert space. We provide experiments on benchmark datasets such as HMDB-51 and UCF-101, fine-grained datasets such as MPII Cooking activities and JHMDB, as well as the recent Kinetics-600. Our results demonstrate the advantages of higher-order pooling schemes that when combined with hand-crafted features (as is standard practice) achieves state-of-the-art accuracy.

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Notes

  1. As we fine-tune the VGG network from a pre-trained ImageNet model, we use \(\beta = 3\) for SMAID in our implementation.

  2. With a slight abuse of previously introduced notations, we assume T to be raw feature trajectories without any scaling or normalization.

  3. Available from https://github.com/feichtenhofer/twostreamfusion.

  4. http://caffe.berkeleyvision.org/.

  5. The VGG-16 and ResNet-152 pre-trained models are publicly available at http://ftp.tugraz.at/pub/feichtenhofer/tsfusion/models/twostream_base/vgg16/http://ftp.tugraz.at/pub/feichtenhofer/tsfusion/models/twostream_base/resnet152/.

  6. http://www.vlfeat.org.

  7. https://deepmind.com/research/open-source/open-source-datasets/kinetics/.

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Acknowledgements

This research was supported by the Australian Research Council (ARC) through the Centre of Excellence for Robotic Vision (CE140100016) and was undertaken with the resources from the National Computational Infrastructure (NCI) at the Australian National University. The authors also thank Mr. Edison Guo (ANU) for helpful discussions.

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  1. Australian Centre for Robotic Vision, The Australian National University, Canberra, Australia

    Anoop Cherian & Stephen Gould

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  1. Anoop Cherian
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Correspondence to Anoop Cherian.

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Cherian, A., Gould, S. Second-order Temporal Pooling for Action Recognition. Int J Comput Vis 127, 340–362 (2019). https://doi.org/10.1007/s11263-018-1111-5

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