26,591 research outputs found
Object movement identification via sparse representation
Object Movement Identification from videos is very challenging, and has got
numerous applications in sports evaluation, video surveillance, elder/child care, etc. In
thisresearch, a model using sparse representation is presented for the human activity detection
from the video data. This is done using a linear combination of atoms from a dictionary and a
sparse coefficient matrix. The dictionary is created using a Spatio Temporal Interest Points
(STIP) algorithm. The Spatio temporal features are extracted for the training video data as well
as the testing video data. The K-Singular Value Decomposition (KSVD)algorithm is used for
learning dictionaries for the trainingvideo dataset. Finally, human action is classified using
aminimum threshold residual value of the corresponding actionclass in the testing video dataset.
Experiments are conducted onthe KTH dataset which contains a number of actions. Thecurrent
approach performed well in classifying activities with asuccess rate of 90%
Action Recognition in Video Using Sparse Coding and Relative Features
This work presents an approach to category-based action recognition in video
using sparse coding techniques. The proposed approach includes two main
contributions: i) A new method to handle intra-class variations by decomposing
each video into a reduced set of representative atomic action acts or
key-sequences, and ii) A new video descriptor, ITRA: Inter-Temporal Relational
Act Descriptor, that exploits the power of comparative reasoning to capture
relative similarity relations among key-sequences. In terms of the method to
obtain key-sequences, we introduce a loss function that, for each video, leads
to the identification of a sparse set of representative key-frames capturing
both, relevant particularities arising in the input video, as well as relevant
generalities arising in the complete class collection. In terms of the method
to obtain the ITRA descriptor, we introduce a novel scheme to quantify relative
intra and inter-class similarities among local temporal patterns arising in the
videos. The resulting ITRA descriptor demonstrates to be highly effective to
discriminate among action categories. As a result, the proposed approach
reaches remarkable action recognition performance on several popular benchmark
datasets, outperforming alternative state-of-the-art techniques by a large
margin.Comment: Accepted to CVPR 201
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
This paper introduces sparse coding and dictionary learning for Symmetric
Positive Definite (SPD) matrices, which are often used in machine learning,
computer vision and related areas. Unlike traditional sparse coding schemes
that work in vector spaces, in this paper we discuss how SPD matrices can be
described by sparse combination of dictionary atoms, where the atoms are also
SPD matrices. We propose to seek sparse coding by embedding the space of SPD
matrices into Hilbert spaces through two types of Bregman matrix divergences.
This not only leads to an efficient way of performing sparse coding, but also
an online and iterative scheme for dictionary learning. We apply the proposed
methods to several computer vision tasks where images are represented by region
covariance matrices. Our proposed algorithms outperform state-of-the-art
methods on a wide range of classification tasks, including face recognition,
action recognition, material classification and texture categorization
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