@inproceedings{d1df2ed3de42484d8a8bdb8f3747de50,
title = "Learning maximum margin temporal warping for action recognition",
abstract = "Temporal misalignment and duration variation in video actions largely influence the performance of action recognition, but it is very difficult to specify effective temporal alignment on action sequences. To address this challenge, this paper proposes a novel discriminative learning-based temporal alignment method, called maximum margin temporal warping (MMTW), to align two action sequences and measure their matching score. Based on the latent structure SVM formulation, the proposed MMTW method is able to learn a phantom action template to represent an action class for maximum discrimination against other classes. The recognition of this action class is based on the associated learned alignment of the input action. Extensive experiments on five benchmark datasets have demonstrated that this MMTW model is able to significantly promote the accuracy and robustness of action recognition under temporal misalignment and variations.",
keywords = "Action Recognition, Depth Camera, Dynamic Temporal Warpping, Temporal Model",
author = "Jiang Wang and Ying Wu",
year = "2013",
doi = "10.1109/ICCV.2013.334",
language = "English (US)",
isbn = "9781479928392",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2688--2695",
booktitle = "Proceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013",
address = "United States",
note = "2013 14th IEEE International Conference on Computer Vision, ICCV 2013 ; Conference date: 01-12-2013 Through 08-12-2013",
}