TY - JOUR
T1 - Tracking with dynamic weighted compressive model
AU - Chen, Ting
AU - Zhang, Yanning
AU - Yang, Tao
AU - Sahli, Hichem
N1 - Publisher Copyright:
© 2016 Elsevier Inc. All rights reserved.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - Fast compressive tracking utilizes a very sparse measurement matrix to capture the appearance model of targets. Such model performs well when the tracked targets are well defined. However, when the targets are low-grain, low-resolution, or small, a single fixed size sparse measurement matrix is not sufficient enough to preserve the image structure of the target. In this work, we propose a multi-sparse measurement matrices scheme along with a weight map to select the best measurement matrix that preserves the image structure of the targets during tracking. The weight map combines a contrast weight and a feature weight to efficiently characterize the target appearance and location. Moreover, a dispersion function is used for the online update of the target template, allowing tracking both the location and scale of the target. Extensive experimental results have demonstrated that the proposed DWCM tracking algorithm outperforms several state-of-the-art tracking algorithms as well as compressive tracker.
AB - Fast compressive tracking utilizes a very sparse measurement matrix to capture the appearance model of targets. Such model performs well when the tracked targets are well defined. However, when the targets are low-grain, low-resolution, or small, a single fixed size sparse measurement matrix is not sufficient enough to preserve the image structure of the target. In this work, we propose a multi-sparse measurement matrices scheme along with a weight map to select the best measurement matrix that preserves the image structure of the targets during tracking. The weight map combines a contrast weight and a feature weight to efficiently characterize the target appearance and location. Moreover, a dispersion function is used for the online update of the target template, allowing tracking both the location and scale of the target. Extensive experimental results have demonstrated that the proposed DWCM tracking algorithm outperforms several state-of-the-art tracking algorithms as well as compressive tracker.
KW - Compressive tracking
KW - Dynamic weighted compressive model
KW - Random matrix
UR - http://www.scopus.com/inward/record.url?scp=84975450984&partnerID=8YFLogxK
U2 - 10.1016/j.jvcir.2016.06.001
DO - 10.1016/j.jvcir.2016.06.001
M3 - 文章
AN - SCOPUS:84975450984
SN - 1047-3203
VL - 39
SP - 253
EP - 265
JO - Journal of Visual Communication and Image Representation
JF - Journal of Visual Communication and Image Representation
ER -