Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Conference paper
Title: Real-time visual tracking using compressive sensing
Author: Li, H.
Shen, C.
Shi, Q.
Citation: Proceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 11): pp.1305-1312
Publisher: IEEE
Publisher Place: Online
Issue Date: 2011
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781457703942
ISSN: 1063-6919
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (24th : 2011 : Colorado Springs, CO, U.S.A.)
Statement of
Hanxi Li, Chunhua Shen, Qinfeng Shi
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 ℓ1 tracker may hamper its applications in real-time processing scenarios. Here we propose Real-time Com-pressive Sensing Tracking (RTCST) by exploiting the signal recovery power of Compressive Sensing (CS). Dimensionality reduction and a customized Orthogonal Matching Pursuit (OMP) algorithm are adopted to accelerate the CS tracking. As a result, our algorithm achieves a realtime speed that is up to 5,000 times faster than that of the ℓ1 tracker. Meanwhile, RTCST still produces competitive (sometimes even superior) tracking accuracy compared to the ℓ1 tracker. Furthermore, for a stationary camera, a refined tracker is designed by integrating a CS-based background model (CSBM) into tracking. This CSBM-equipped tracker, termed RTCST-B, outperforms most state-of-the-art trackers in terms of both accuracy and robustness. Finally, our experimental results on various video sequences, which are verified by a new metric - Tracking Success Probability (TSP), demonstrate the excellence of the proposed algorithms.
Rights: Copyright status unknown
RMID: 0020115469
DOI: 10.1109/CVPR.2011.5995483
Description (link):
Appears in Collections:Computer Science publications

Files in This Item:
File Description SizeFormat 
RA_hdl_70440.pdfRestricted Access439.67 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.