Title

Learning A Deep Model For Human Action Recognition From Novel Viewpoints

Keywords

Cross-view; dense trajectories; view knowledge transfer

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 set of non-linear transformations that connects the views. The R-NKTM is learned from 2D projections of 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.

Publication Date

3-1-2018

Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

40

Issue

3

Number of Pages

667-681

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TPAMI.2017.2691768

Socpus ID

85041966324 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85041966324

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