NDLI: Probabilistic Visual Tracking via Robust Template Matching and Incremental Subspace Update

Content Provider	IEEE Xplore Digital Library
Author	Xue Mei Zhou, S.K. Porikli, F.
Copyright Year	2007
Description	Author affiliation: Univ. of Maryland, College Park (Xue Mei)
Abstract	In this paper, we present a probabilistic algorithm for visual tracking that incorporates robust template matching and incremental sub-space update. There are two template matching methods used in the tracker: one is robust to small perturbation and the other to background clutter. Each method yields a probability of matching. Further, the templates are modeled using mixed probabilities and updated once the templates in the library cannot capture the variation of object appearance. We also model the tracking history using a nonlinear subspace that is described by probabilistic kernel principal components analysis, which provides a third probability. The most-recent tracking result is added to the nonlinear subspace incrementally. This update is performed efficiently by augmenting the kernel Gram matrix with one row and one column. The product of the three probabilities is defined as the observation likelihood used in a particle filter to derive the tracking result. Experimental results demonstrate the efficiency and effectiveness of the proposed algorithm.
Starting Page	1818
Ending Page	1821
File Size	286764
Page Count	4
File Format	PDF
ISBN	1424410169
DOI	10.1109/ICME.2007.4285026
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Particle filters Kernel Target tracking History Principal component analysis Libraries State estimation Computer vision Monte Carlo methods
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

INCREMENTAL SUBSPACE UPDATE (2008)

Multi-Target Tracking using Separated Importance Sampling Particle Filters with Joint Image Likelihood

Visual tracking via incremental Log-Euclidean Riemannian subspace learning

Affine Modeling of Targets in Video Sequences by Particle Filters

Visual tracking based on incremental two-dimensional Maximum Margin Criterion

Probabilistic shape vision for embedded systems

Robust visual tracking via weighted incremental subspace learning

Adaptive target color model updating for visual tracking using particle filter

Robust visual tracking using incremental appearance descriptor update

Probabilistic Visual Tracking via Robust Template Matching and Incremental Subspace Update

Similar Documents

INCREMENTAL SUBSPACE UPDATE (2008)

Multi-Target Tracking using Separated Importance Sampling Particle Filters with Joint Image Likelihood

Visual tracking via incremental Log-Euclidean Riemannian subspace learning

Affine Modeling of Targets in Video Sequences by Particle Filters

Visual tracking based on incremental two-dimensional Maximum Margin Criterion

Probabilistic shape vision for embedded systems

Robust visual tracking via weighted incremental subspace learning

Adaptive target color model updating for visual tracking using particle filter

Robust visual tracking using incremental appearance descriptor update

Probabilistic Visual Tracking via Robust Template Matching and Incremental Subspace Update