Motion Part Regularization: Improving Action Recognition via Trajectory Selection

Bingbing Ni, Pierre Moulin, Xiaokang Yang, Shuicheng Yan; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3698-3706

Abstract


Dense local motion features such as dense trajectories have been widely used in action recognition. For most actions, only a few local features (e.g., critical movements of the hand, arm, leg etc.) are responsible to the action label. Therefore, discovering important motion part will lead to a more discriminative and compact action representation. Inspired by the recent advance in sentence regularization for text classification, we introduce a Motion Part Regularization framework to mining discriminative semi-local groups of dense trajectories. First, motion part candidates are generated by spatio-temporal grouping of densely sampled trajectories. Then, we develop a learning objective function which encourages sparse selection for these trajectory groups in conjunction with a discriminative term. We propose an alternative optimization algorithm to efficiently solve this objective function by introducing a set of auxiliary variables. The learned trajectory group weights are further utilized for weighted bag-of-feature representation for unknown action samples. The proposed motion part regularization framework achieves the state-of-the-art performances on several action recognition benchmarks.

Related Material


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[bibtex]
@InProceedings{Ni_2015_CVPR,
author = {Ni, Bingbing and Moulin, Pierre and Yang, Xiaokang and Yan, Shuicheng},
title = {Motion Part Regularization: Improving Action Recognition via Trajectory Selection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}