TY - JOUR
T1 - Locator-Checker-Scaler Object Tracking Using Spatially Ordered and Weighted Patch Descriptor
AU - Kim, Han Ul
AU - Kim, Chang-Su
N1 - Funding Information:
This work was supported in part by the National Research Foundation of Korea (NRF) through the Korea Government (MSIP) under Grant NRF-2015R1A2A1A10055037, and in part by the MSIP, Korea, through the ITRC support program supervised by the Institute for Information & communications Technology Promotion under Grant IITP-2017-2016-0-00464.
Publisher Copyright:
© 1992-2012 IEEE.
PY - 2017/8
Y1 - 2017/8
N2 - In this paper, we propose a simple yet effective object descriptor and a novel tracking algorithm to track a target object accurately. For the object description, we divide the bounding box of a target object into multiple patches and describe them with color and gradient histograms. Then, we determine the foreground weight of each patch to alleviate the impacts of background information in the bounding box. To this end, we perform random walk with restart (RWR) simulation. We then concatenate the weighted patch descriptors to yield the spatially ordered and weighted patch (SOWP) descriptor. For the object tracking, we incorporate the proposed SOWP descriptor into a novel tracking algorithm, which has three components: locator, checker, and scaler (LCS). The locator and the scaler estimate the center location and the size of a target, respectively. The checker determines whether it is safe to adjust the target scale in a current frame. These three components cooperate with one another to achieve robust tracking. Experimental results demonstrate that the proposed LCS tracker achieves excellent performance on recent benchmarks.
AB - In this paper, we propose a simple yet effective object descriptor and a novel tracking algorithm to track a target object accurately. For the object description, we divide the bounding box of a target object into multiple patches and describe them with color and gradient histograms. Then, we determine the foreground weight of each patch to alleviate the impacts of background information in the bounding box. To this end, we perform random walk with restart (RWR) simulation. We then concatenate the weighted patch descriptors to yield the spatially ordered and weighted patch (SOWP) descriptor. For the object tracking, we incorporate the proposed SOWP descriptor into a novel tracking algorithm, which has three components: locator, checker, and scaler (LCS). The locator and the scaler estimate the center location and the size of a target, respectively. The checker determines whether it is safe to adjust the target scale in a current frame. These three components cooperate with one another to achieve robust tracking. Experimental results demonstrate that the proposed LCS tracker achieves excellent performance on recent benchmarks.
KW - Visual tracking
KW - bounding box descriptor
KW - discriminative tracker
KW - object tracking
KW - tracking with multiple estimators
UR - http://www.scopus.com/inward/record.url?scp=85020756006&partnerID=8YFLogxK
U2 - 10.1109/TIP.2017.2706064
DO - 10.1109/TIP.2017.2706064
M3 - Article
C2 - 28541204
AN - SCOPUS:85020756006
SN - 1057-7149
VL - 26
SP - 3817
EP - 3830
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 8
M1 - 7931611
ER -