NDLI: Discovery of Sparse Contextual Relationships for Activity Recognition in Continuous Videos

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Discovery of Sparse Contextual Relationships for Activity Recognition in Continuous Videos

Content Provider	ACM Digital Library
Author	Nayak, Nandita M. Roy-Chowdhury, Amit K. Srinivasan, Ramya
Abstract	Recent trends in activity recognition have shown that structured learning methods are useful in modeling spatio-temporal relationships, often termed as context in continuous videos. However, learning an efficient set of relationships or an ideal structure for representation of context in these videos still remains a challenging problem. While a continuous video consists of several activities, the contextual relationships between these activities are relatively sparse. We propose a method which aims to discover these sparse relationships using an L1-regularization based automatic structure discovery of a graphical model representing the video. We propose a 2-layered graphical model, where the lower level models feature information and the higher level models spatiotemporal context. The features are modeled using space-time interest points, while the relationships between activities are modeled using an undirected graphical model. Sparsity is imposed on the edges of the graph so as to model a sparse set of relationships. Experiments have been conducted on complex activity recognition datasets to demonstrate the superior performance of our approach in classification of continuous activities.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450330619
DOI	10.1145/2683483.2683495
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-12-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	L1-regularized optimization Activity recognition Continuous videos Sparse learning Hierarchical mrf
Content Type	Text
Resource Type	Article

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