Locator-Checker-Scaler Object Tracking Using Spatially Ordered and Weighted Patch Descriptor

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 determ...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2017-08, Vol.26 (8), p.3817-3830
Main Authors: Kim, Han-Ul, Kim, Chang-Su
Format: Article
Language:English
Subjects:
Algorithms
Benchmark testing
Benchmarks
bounding box descriptor
Color
Computer simulation
discriminative tracker
Estimation
Histograms
object tracking
Patches (structures)
Random walk
Robustness
Target tracking
Tracking
tracking with multiple estimators
Visual tracking
Visualization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2017.2706064