|
|
|
|
|
|
|
|
Title
|
Approximate proximal gradient-based correlation filter for target tracking in videos: A unified approach
|
|
Sub-Title |
|
|
Subject |
Object Recognition, Object Tracking, MACH, APG
|
|
Sub-Subject |
|
|
Author |
Haris Masood, Saad Rehman, Aimal Khan, Farhan Riaz
|
|
Publish Year |
2019 |
|
Supervisor |
|
|
Diss#. |
https://doi.org/10.1007/s13369-019-03861-3 |
|
Chapters |
|
|
Pages |
9363–9380 |
|
Text Language |
English |
|
Accession |
|
|
Library Section |
Research Article |
|
Abstract |
Video cameras are among the most commonly used devices throughout the world which results in imaging technology being
one of the most important areas for research and development. Imaging technology requires constant research as it is used
in crucial applications such as video conferencing and surveillance. In the field of image processing, motion detection and
estimation are fundamental steps in extracting information on objects segmented from their backgrounds. In this paper, a
cohesive approach is presented that uses two algorithms for motion estimation and detection. The proposed method is able to
detect moving objects using maximum average correlation height (MACH) filter. Upon obtaining the accurate coordinates of
an object of interest from theMACH filter, the next part of the algorithm starts tracking the object. For tracking, a particle filter
is used to estimate the motion of the object using a Markov chain. To enhance the accuracy of particle filter, an approximate
proximal g
|
|
Catalogue
|
Books
|
|
Periodicals
|
Newspapers
|
Publications
|
|
Research Papers
|
|
|
|
|