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Visual Tracking by Sampling in Part Space

Huang, Lianghua; Ma, Bo; Shen, Jianbing; He, Hui; Shao, Ling; Porikli, Fatih

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In this paper, we present a novel part-based visual tracking method from the perspective of probability sampling. Specifically, we represent the target by a part space with two online learned probabilities to capture the structure of the target. The proposal distribution memorizes the historical performance of different parts, and it is used for the first round of part selection. The acceptance probability validates the specific tracking stability of each part in a frame, and it determines...[Show more]

dc.contributor.authorHuang, Lianghua
dc.contributor.authorMa, Bo
dc.contributor.authorShen, Jianbing
dc.contributor.authorHe, Hui
dc.contributor.authorShao, Ling
dc.contributor.authorPorikli, Fatih
dc.date.accessioned2021-10-13T22:29:20Z
dc.identifier.issn1057-7149
dc.identifier.urihttp://hdl.handle.net/1885/250786
dc.description.abstractIn this paper, we present a novel part-based visual tracking method from the perspective of probability sampling. Specifically, we represent the target by a part space with two online learned probabilities to capture the structure of the target. The proposal distribution memorizes the historical performance of different parts, and it is used for the first round of part selection. The acceptance probability validates the specific tracking stability of each part in a frame, and it determines whether to accept its vote or to reject it. By doing this, we transform the complex online part selection problem into a probability learning one, which is easier to tackle. The observation model of each part is constructed by an improved supervised descent method and is learned in an incremental manner. Experimental results on two benchmarks demonstrate the competitive performance of our tracker against state-of-the-art methods.
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 61472036, in part by the National Basic Research Program of China (973 Program) under Grant 2013CB328805, and in part by the Australian Research Council’s Discovery Projects Funding Scheme under Grant DP150104645. Specialized Fund for Joint Building Program of the Beijing Municipal Education Commission.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.rights© 2017 IEEE
dc.sourceIEEE Transactions on Image Processing
dc.subjectVisual tracking
dc.subjectpart space
dc.subjectsampling
dc.titleVisual Tracking by Sampling in Part Space
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume26
dc.date.issued2017
local.identifier.absfor080106 - Image Processing
local.identifier.ariespublicationa383154xPUB8870
local.publisher.urlhttps://www.ieee.org/
local.type.statusPublished Version
local.contributor.affiliationHuang, Lianghua, Beijing Institute of Technology
local.contributor.affiliationMa, Bo, Beijing Institute of Technology
local.contributor.affiliationShen, Jianbing, Beijing Lab of Intelligent Information Technology
local.contributor.affiliationHe, Hui, Beijing Institute of Technology
local.contributor.affiliationShao, Ling, University of East Anglia
local.contributor.affiliationPorikli, Fatih, College of Engineering and Computer Science, ANU
local.description.embargo2099-12-31
dc.relationhttp://purl.org/au-research/grants/arc/DP150104645
local.bibliographicCitation.issue12
local.bibliographicCitation.startpage5800
local.bibliographicCitation.lastpage5810
local.identifier.doi10.1109/TIP.2017.2745204
dc.date.updated2020-11-23T11:26:40Z
local.identifier.scopusID2-s2.0-85028706236
CollectionsANU Research Publications

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