Non-linear Temporal Subspace Representations for Activity Recognition
Name
1803.11064.pdf
Description
Submitted version
Size
769.37 KB
Format
Adobe PDF
Checksum (MD5)
1fa7bfdba659cd9e655a2aac0ebc3856
Author(s)
Cherian, Anoop
Sra, Suvrit
Gould, Stephen
Hartley, Richard
Date Issued
June 2018
Journal
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Cherian, Anoop, Sra, Suvrit, Gould, Stephen and Hartley, Richard. 2018. "Non-linear Temporal Subspace Representations for Activity Recognition." Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Version
Original manuscript
Abstract
© 2018 IEEE. Representations that can compactly and effectively capture the temporal evolution of semantic content are important to computer vision and machine learning algorithms that operate on multi-variate time-series data. We investigate such representations motivated by the task of human action recognition. Here each data instance is encoded by a multivariate feature (such as via a deep CNN) where action dynamics are characterized by their variations in time. As these features are often non-linear, we propose a novel pooling method, kernelized rank pooling, that represents a given sequence compactly as the pre-image of the parameters of a hyperplane in a reproducing kernel Hilbert space, projections of data onto which captures their temporal order. We develop this idea further and show that such a pooling scheme can be cast as an order-constrained kernelized PCA objective. We then propose to use the parameters of a kernelized low-rank feature subspace as the representation of the sequences. We cast our formulation as an optimization problem on generalized Grassmann manifolds and then solve it efficiently using Riemannian optimization techniques. We present experiments on several action recognition datasets using diverse feature modalities and demonstrate state-of-the-art results.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cvpr.2018.00234
