Object tracking using compressive local appearance model with ℓ1-regularisation

A novel compressive local appearance model-based object tracking algorithm is presented to address challenging issues in object tracking. To efficiently preserve image patches of an object and reduce the dimensionality, a random projection-based feature selection method is introduced. Modelling the...

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Bibliographic Details
Published in:Electronics letters Vol. 50; no. 6; pp. 444 - 446
Main Authors: Kim, Hyuncheol, Paik, Joonki
Format: Journal Article
Language:English
Published: Stevenage The Institution of Engineering and Technology 13.03.2014
Institution of Engineering and Technology
Subjects:
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Data processing. List processing. Character string processing
Detection, estimation, filtering, equalization, prediction
dimensionality reduction
Exact sciences and technology
feature selection
Image and vision processing and display technology
image patches
image representation
Information, signal and communications theory
matrix algebra
Memory organisation. Data processing
modified likelihood function
novel compressive local appearance model‐based object tracking algorithm
object tracking
Pattern recognition. Digital image processing. Computational geometry
random projection‐based feature selection method
reconstruction error
residual matrix
Signal and communications theory
Signal, noise
Software
sparse representation
Telecommunications and information theory
â„“1‐regularisation problem
novel compressive local appearance model-based object tracking algorithm
modified likelihood function
residual matrix
matrix algebra
reconstruction error
dimensionality reduction
random projection-based feature selection method
â„“1-regularisation problem
image representation
object tracking
image patches
feature selection
sparse representation
Occlusion
Computer vision
Data analysis
Dimensionality
Target tracking
Tracking
Image processing
Local theory
Long term
Modeling
Projection method
Dimension reduction
Experimental result
Selection criterion
Sparse representation
Occultation
Likelihood function
ISSN:0013-5194, 1350-911X, 1350-911X
Online Access:Get full text
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Description
Summary:A novel compressive local appearance model-based object tracking algorithm is presented to address challenging issues in object tracking. To efficiently preserve image patches of an object and reduce the dimensionality, a random projection-based feature selection method is introduced. Modelling the object's appearance using a sparse representation over a set of templates leads to an ℓ1-regularisation problem. To solve this problem, both the reconstruction error and the residual matrix are considered which play a key role in tracking an object with severe appearance variations using the modified likelihood function. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art tracking methods in terms of dealing with long-term partial occlusion, deformation and rotation.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2013.2763