CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Automatic Color Object Detection and Learning using Continuously Adaptive Mean Shift with Color, Scale and Direction

by Kishor S. Jeve, Ashok T. Gaikwad, Pravin L. Yannawar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 165 - Number 11
Year of Publication: 2017
Authors: Kishor S. Jeve, Ashok T. Gaikwad, Pravin L. Yannawar
10.5120/ijca2017914042

Kishor S. Jeve, Ashok T. Gaikwad, Pravin L. Yannawar . Automatic Color Object Detection and Learning using Continuously Adaptive Mean Shift with Color, Scale and Direction. International Journal of Computer Applications. 165, 11 ( May 2017), 1-3. DOI=10.5120/ijca2017914042

@article{ 10.5120/ijca2017914042,
author = { Kishor S. Jeve, Ashok T. Gaikwad, Pravin L. Yannawar },
title = { Automatic Color Object Detection and Learning using Continuously Adaptive Mean Shift with Color, Scale and Direction },
journal = { International Journal of Computer Applications },
issue_date = { May 2017 },
volume = { 165 },
number = { 11 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-3 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume165/number11/27614-2017914042/ },
doi = { 10.5120/ijca2017914042 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:12:10.185939+05:30
%A Kishor S. Jeve
%A Ashok T. Gaikwad
%A Pravin L. Yannawar
%T Automatic Color Object Detection and Learning using Continuously Adaptive Mean Shift with Color, Scale and Direction
%J International Journal of Computer Applications
%@ 0975-8887
%V 165
%N 11
%P 1-3
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The tracking of motion of color object and detection of objects in real time video sequences is the important task in computer vision, video processing, image processing, intelligent system, etc. In this paper, we proposed Continuously Adaptive Mean Shift (CAMSHIFT) tracking with Color, Scale and Direction. In the CAMSHIFT the scale of the kernel is automatically altered in accordance with the object scale, and also follows the object direction accurately, when an initial adoption of the object shape is provided. The proposed method tracks the object accurately and efficiently.

References
  1. Changjiang Yang, Ramani Duraiswami and Larry Davis, 2005. Efficient Mean-Shift Tracking via a New Similarity Measure.IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
  2. R. Venkatesh Babu, Anamitra Makur, 2007. Kernel-Based Spatial-Color Modeling For Fast Moving Object Tracking. IEEE International Conference on Acoustics, Speech and Signal Processing.
  3. D. Comaniciu and P Meer, 2002.Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence.
  4. Q. Qifeng, D. Zhang, and Y. Peng, 2007. An adaptive selection of the scale and orientation in kernel based tracking. IEEE Conference on Signal-Image Technologies and Internet-Based Systems.
  5. Cheng, Yizong, 1995. Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence.
  6. Katharina Quast and Andr´e Kaup, 2010. Shape Adaptive Mean shift Object Tracking Using Gaussian Mixture Model. Image Analysis for Multimedia Interactive Services (WIAMIS).
  7. M. Sohail Khalid, M. Umar Ilyas, M. Saquib Sarfaraz, M. Asim Ajaz, 2006. Bhattacharyya Coefficient in Correlation of Gray Scale Objects. Journal of Multimedia.
  8. Nwe Nwe Soe, 2015.Image Matching Scheme by using Bhattacharyya Coefficient Algorithm. International Journal of Innovative Research in Computer and Communication Engineering.
  9. M. J. Black and A. D. Jepson, 1998. Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. International Journal of Computer Vision.
  10. S. Avidan, 2001. Support vector tracking. In Proc. IEEE Conf. Comp. Vision Pattern Recognition, volume I.
  11. A. Elgammal, R. Duraiswami, and L. Davis, 2003. Probabilistic tracking in joint feature-spatial spaces. In Proc. IEEE Conf. Comp. Vision Pattern Recognition, volume I.
  12. Changjiang Yang, Ramani Duraiswami and Larry Davis, 2005. Efficient Mean-Shift Tracking via a New Similarity Measure. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Index Terms

Computer Science
Information Sciences

Keywords

Continuously Adaptive mean shift object tracking color scale direction kernel.

Powered by PhDFocusTM