Human Action Attribute Learning From Video Data Using Low-Rank Representations release_rnfspdvwtvaozmnjeckqivrl7m

by Tong Wu, Prudhvi Gurram, Raghuveer M. Rao, Waheed U. Bajwa

Released as a article .

2016  

Abstract

Representation of human actions as a sequence of human body movements or action attributes enables the development of models for human activity recognition and summarization. We present an extension of the low-rank representation (LRR) model, termed the clustering-aware structure-constrained low-rank representation (CS-LRR) model, for unsupervised learning of human action attributes from video data. Our model is based on the union-of-subspaces (UoS) framework, and integrates spectral clustering into the LRR optimization problem for better subspace clustering results. We lay out an efficient linear alternating direction method to solve the CS-LRR optimization problem. We also introduce a hierarchical subspace clustering approach, termed hierarchical CS-LRR, to learn the attributes without the need for a priori specification of their number. By visualizing and labeling these action attributes, the hierarchical model can be used to semantically summarize long video sequences of human actions at multiple resolutions. A human action or activity can also be uniquely represented as a sequence of transitions from one action attribute to another, which can then be used for human action recognition. We demonstrate the effectiveness of the proposed model for semantic summarization and action recognition through comprehensive experiments on five real-world human action datasets.
In text/plain format

Archived Files and Locations

application/pdf  4.8 MB
file_zp3u3n6b6bglff6hahsq3pyf7y
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2016-12-23
Version   v1
Language   en ?
arXiv  1612.07857v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: fedd1860-c385-44e6-9bbc-4d5349949150
API URL: JSON