NDLI: Weighted Part Context Learning for Visual Tracking

Content Provider	IEEE Xplore Digital Library
Author	Guibo Zhu Jinqiao Wang Chaoyang Zhao Hanqing Lu
Copyright Year	1992
Abstract	Context information is widely used in computer vision for tracking arbitrary objects. Most of the existing studies focus on how to distinguish the object of interest from background or how to use keypoint-based supporters as their auxiliary information to assist them in tracking. However, in most cases, how to discover and represent both the intrinsic properties inside the object and the surrounding context is still an open problem. In this paper, we propose a unified context learning framework that can effectively capture spatiotemporal relations, prior knowledge, and motion consistency to enhance tracker's performance. The proposed weighted part context tracker (WPCT) consists of an appearance model, an internal relation model, and a context relation model. The appearance model represents the appearances of the object and the parts. The internal relation model utilizes the parts inside the object to directly describe the spatiotemporal structure property, while the context relation model takes advantage of the latent intersection between the object and background regions. Then, the three models are embedded in a max-margin structured learning framework. Furthermore, prior label distribution is added, which can effectively exploit the spatial prior knowledge for learning the classifier and inferring the object state in the tracking process. Meanwhile, we define online update functions to decide when to update WPCT, as well as how to reweight the parts. Extensive experiments and comparisons with the state of the arts demonstrate the effectiveness of the proposed method.
Sponsorship	IEEE Signal Processing Society
Starting Page	5140
Ending Page	5151
Page Count	12
File Size	6419627
File Format	PDF
ISSN	10577149
Volume Number	24
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Context modeling Target tracking Bismuth Visualization Support vector machines Structure Leaning Visual Tracking Part Context model structure leaning Visual tracking part context model
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design 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

Visual Tracking With Spatio-Temporal Dempster–Shafer Information Fusion

Robust Deformable and Occluded Object Tracking With Dynamic Graph

Robust Online Learned Spatio-Temporal Context Model for Visual Tracking

Robust Superpixel Tracking

Learning Adaptive Metric for Robust Visual Tracking

BIT: Biologically Inspired Tracker

Robust Visual Tracking via Sparsity-Induced Subspace Learning

Horror Image Recognition Based on Context-Aware Multi-Instance Learning

Data-Driven Spatially-Adaptive Metric Adjustment for Visual Tracking

Weighted Part Context Learning for Visual Tracking

Similar Documents

Visual Tracking With Spatio-Temporal Dempster–Shafer Information Fusion

Robust Deformable and Occluded Object Tracking With Dynamic Graph

Robust Online Learned Spatio-Temporal Context Model for Visual Tracking

Robust Superpixel Tracking

Learning Adaptive Metric for Robust Visual Tracking

BIT: Biologically Inspired Tracker

Robust Visual Tracking via Sparsity-Induced Subspace Learning

Horror Image Recognition Based on Context-Aware Multi-Instance Learning

Data-Driven Spatially-Adaptive Metric Adjustment for Visual Tracking

Weighted Part Context Learning for Visual Tracking