Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Real-time visual tracking using compressive sensing

Date

Authors

Li, Hanxi

Shen, Chunhua

Shi, Qinfeng

Publisher

IEEE Computer Society

Abstract

The ℓ1 tracker obtains robustness by seeking a sparse representation of the tracking object via ℓ1 norm minimization. However, the high computational complexity involved in the 1 tracker may hamper its applications in real-time processing scenarios. H

Keywords

Keywords: Background model; Dimensionality reduction; Orthogonal matching pursuit; Real time; Realtime processing; Signal recovery; Sparse representation; Stationary cameras; Tracking accuracy; Tracking objects; Video sequences; Visual Tracking; Algorithms; Computa

URI

http://hdl.handle.net/1885/63712

Collections

ANU Research Publications

Source

Graph connectivity in sparse subspace clustering

Type

Conference paper

DOI

10.1109/CVPR.2011.5995483

Restricted until

2037-12-31

Downloads

File

Description

01_Li_Real-time_visual_tracking_2011.pdf (439.66 KB)