Learning a Non-Linear Knowledge Transfer Model for Cross-View Action Recognition

Hossein Rahmani, Ajmal Mian; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 2458-2466

Abstract


This paper concerns action recognition from unseen and unknown views. We propose unsupervised learning of a non-linear model that transfers knowledge from multiple views to a canonical view. The proposed Non-linear Knowledge Transfer Model (NKTM) is a deep network, with weight decay and sparsity constraints, which finds a shared high-level virtual path from videos captured from different unknown viewpoints to the same canonical view. The strength of our technique is that we learn a single NKTM for all actions and all camera viewing directions. Thus, NKTM does not require action labels during learning and knowledge of the camera viewpoints during training or testing. NKTM is learned once only from dense trajectories of synthetic points fitted to mocap data and then applied to real video data. Trajectories are coded with a general codebook learned from the same mocap data. NKTM is scalable to new action classes and training data as it does not require re-learning. Experiments on the IXMAS and N-UCLA datasets show that NKTM outperforms existing state-of-the-art methods for cross-view action recognition.

Related Material


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[bibtex]
@InProceedings{Rahmani_2015_CVPR,
author = {Rahmani, Hossein and Mian, Ajmal},
title = {Learning a Non-Linear Knowledge Transfer Model for Cross-View Action Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}