Show simple item record

dc.contributor.authorXiong, Fengchao
dc.contributor.authorZhou, Jun
dc.contributor.authorQian, Yuntao
dc.description.abstractTraditional color images only depict color intensities in red, green and blue channels, often making object trackers fail in challenging scenarios, e.g., background clutter and rapid changes of target appearance. Alternatively, material information of targets contained in large amount of bands of hyperspectral images (HSI) is more robust to these difficult conditions. In this paper, we conduct a comprehensive study on how material information can be utilized to boost object tracking from three aspects: dataset, material feature representation and material based tracking. In terms of dataset, we construct a dataset of fully-annotated videos, which contain both hyperspectral and color sequences of the same scene. Material information is represented by spectral-spatial histogram of multidimensional gradients, which describes the 3D local spectral-spatial structure in an HSI, and fractional abundances of constituted material components which encode the underlying material distribution. These two types of features are embedded into correlation filters, yielding material based tracking. Experimental results on the collected dataset show the potentials and advantages of material based object tracking.
dc.relation.ispartofjournalIEEE Transactions on Image Processing
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchCognitive Sciences
dc.subject.keywordsScience & Technology
dc.subject.keywordsComputer Science, Artificial Intelligence
dc.subject.keywordsEngineering, Electrical & Electronic
dc.subject.keywordsComputer Science
dc.titleMaterial Based Object Tracking in Hyperspectral Videos
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationXiong, F; Zhou, J; Qian, Y, Material Based Object Tracking in Hyperspectral Videos, IEEE Transactions on Image Processing, 2020, 29, pp. 3719-3733
gro.hasfulltextNo Full Text
gro.griffith.authorZhou, Jun

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record