NDLI: Robust Visual Tracking and Vehicle Classification via Sparse Representation

Content Provider	IEEE Xplore Digital Library
Author	Xue Mei Haibin Ling
Copyright Year	1979
Abstract	In this paper, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise, and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target in a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an ℓ₁-regularized least-squares problem. Then, the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework. Two strategies are used to further improve the tracking performance. First, target templates are dynamically updated to capture appearance changes. Second, nonnegativity constraints are enforced to filter out clutter which negatively resembles tracking targets. We test the proposed approach on numerous sequences involving different types of challenges, including occlusion and variations in illumination, scale, and pose. The proposed approach demonstrates excellent performance in comparison with previously proposed trackers. We also extend the method for simultaneous tracking and recognition by introducing a static template set which stores target images from different classes. The recognition result at each frame is propagated to produce the final result for the whole video. The approach is validated on a vehicle tracking and classification task using outdoor infrared video sequences.
Sponsorship	IEEE Computer Society
Page Count	14
File Size	2939034
Starting Page	2259
Ending Page	2272
File Format	PDF
ISSN	01628828
Volume Number	33
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Pixel Robustness Visualization Noise Least squares approximation \ell_1 minimization. Visual tracking sparse representation compressive sensing simultaneous tracking and recognition particle filter
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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 Library 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

Robust Visual Tracking Using Structurally Random Projection and Weighted Least Squares

Illumination Recovery From Image With Cast Shadows Via Sparse Representation

Robust Multitask Multiview Tracking in Videos

Infrared Target Tracking Based on Robust Low-Rank Sparse Learning

Visual tracking using IPCA and sparse representation

Audio Assisted Robust Visual Tracking With Adaptive Particle Filtering

Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation

Fast Compressive Tracking

Robust Visual Tracking via Sparsity-Induced Subspace Learning

Robust Visual Tracking and Vehicle Classification via Sparse Representation

Similar Documents

Robust Visual Tracking Using Structurally Random Projection and Weighted Least Squares

Illumination Recovery From Image With Cast Shadows Via Sparse Representation

Robust Multitask Multiview Tracking in Videos

Infrared Target Tracking Based on Robust Low-Rank Sparse Learning

Visual tracking using IPCA and sparse representation

Audio Assisted Robust Visual Tracking With Adaptive Particle Filtering

Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation

Fast Compressive Tracking

Robust Visual Tracking via Sparsity-Induced Subspace Learning

Robust Visual Tracking and Vehicle Classification via Sparse Representation