Learning a deep model for human action recognition from novel viewpoints

Rahmani, Hossein and Mian, Ajmal and Shah, Mubarak (2018) Learning a deep model for human action recognition from novel viewpoints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40 (3). pp. 667-681. ISSN 0162-8828

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Abstract

Recognizing human actions from unknown and unseen (novel) views is a challenging problem. We propose a Robust Non-Linear Knowledge Transfer Model (R-NKTM) for human action recognition from novel views. The proposed R-NKTM is a deep fully-connected neural network that transfers knowledge of human actions from any unknown view to a shared high-level virtual view by finding a non-linear virtual path that connects the views. The R-NKTM is learned from dense trajectories of synthetic 3D human models fitted to real motion capture data and generalizes to real videos of human actions. The strength of our technique is that we learn a single R-NKTM for all actions and all viewpoints for knowledge transfer of any real human action video without the need for re-training or fine-tuning the model. Thus, R-NKTM can efficiently scale to incorporate new action classes. R-NKTM is learned with dummy labels and does not require knowledge of the camera viewpoint at any stage. Experiments on three benchmark cross-view human action datasets show that our method outperforms existing state-of-the-art.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Additional Information:
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Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1707
Subjects:
ID Code:
126298
Deposited By:
Deposited On:
11 Jul 2018 10:08
Refereed?:
Yes
Published?:
Published
Last Modified:
25 Sep 2020 03:47