NDLI: On the improvement of foreground–background model-based object tracker

Content Provider	IET Digital Library
Author	Hanif, Muhammad Shehzad Ahmad, Shafiq Khurshid, Khurram
Abstract	In this study, the authors propose two kinds of improvements to a baseline tracker that employs the tracking-by-detection framework. First, they explore different feature spaces by employing features commonly used in object detection to improve the performance of detector in feature space. Second, they propose a robust scale estimation algorithm that estimates the size of the object in the current frame. Their experimental results on the challenging online tracking benchmark-13 dataset show that reduced dimensionality histogram of oriented gradients boosts the performance of the tracker. The proposed scale estimation algorithm provides a significant gain and reduces the failure of the tracker in challenging scenarios. The improved tracker is compared with 13 state-of-the-art trackers. The quantitative and qualitative results show that the performance of the tracker is comparable with the state of the art against initialisation errors, variations in illumination, scale and motion, out-of-plane and in-plane rotations, deformations and low resolution.
Starting Page	488
Ending Page	496
Page Count	9
ISSN	17519632
Volume Number	11
e-ISSN	17519640
Issue Number	Issue 6, Sep (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/11/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0487
Journal	IET Computer Vision
Publisher Date	2017-05-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Feature Space Foreground–background Model-based Object Tracker Object Detection Object Tracking Online Tracking Benchmark-13 Dataset Optical, Image And Video Signal Processing Reduced Dimensionality Histogram of Oriented Gradients Robust Scale Estimation Algorithm Tracking-by-detection Framework
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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