Contact Us

For Marketing, Sales and Subscriptions Inquiries
2637 E Atlantic Blvd #43110
Pompano Beach, FL 33062

Conference List

Moving Target Tracking in Infrared Image Sequences Based on Differential Kernel Covariance Descriptor



Hamidullah BINOL


Huseyin CUKUR

Burak ALPTEKIN 1 ,

Hamidullah BINOL 1 Huseyin CUKUR 1 
  1. Department of Electronics and Communications Engineering, Yildiz Technical University Istanbul, Turkey 1

Pages: 92-99

DOI: 10.18488/journal.63/2016.4.5/

Share :

Article History:

Received: 02 November, 2016
Revised: 02 December, 2016
Accepted: 07 December, 2016
Published: 14 December, 2016


Forward looking infrared (FLIR) imaging has been used in many areas of research and everyday life, but it has been mostly employed in military and security domains. In these fields, remote infrared target tracking is a crucial element for surveillance. However, long-range captured IR image sequences generally have poor contrast, variable illumination, and high background clutter. These challenges make target tracking difficult. This paper suggests a technique for target tracking in different ranges in challenging FLIR image sequences, based on Differential Kernel Covariance Descriptor (DKCD). This new method diminishes rotation and illumination variation effects. The proposed technique calculates the differential kernel matrix of reference target by using various statistical and spatial features such as first and second derivatives, location information, and the intensity value of pixels. Later, the differential covariance matrix is constructed by using different pixel features and applying the appropriate kernel function to the matrix. Thanks to the kernel functions, the algorithm redefines the target's differential spatial features in Hilbert space. This process makes the descriptor non-linear. The predicted position of the target is calculated with the nearest neighbor algorithm in the candidate regions in the sub-frame. The performance of the suggested single target tracking system is then tested on challenging real-life video sequences.
Contribution/ Originality
This paper proposes a new nonlinear descriptor which mainly uses kernel covariance matrix based on difference of features. The technique minimizes the effect of pose variation, illumination, size, and background changes.


Differential kernel covariance descriptor, Visual tracking, Infrared image sequence.



  1. P. Gutman and M. Velger, "Tracking targets using adaptive kalman filtering," EEE Transactions on Aerospace and Electronic Systems, vol. 26, pp. 691-699, 1990.
  2. D. Comaniciu, V. Ramesh, and P. Meer, "Real-time tracking of non-rigid objects usingmean shift," presented at the Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR Hilton Head Island, 2000.
  3. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 603 – 619, 2002.
  4. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 564-577, 2003.
  5. K. Nummiaro, K.-M. Esther, and V. G. Luc, "An adaptive color-based particle filter," Image and Vision Computing, vol. 21, pp. 99-110, 2003.
  6. A. Dawoud, M. Alam, A. Bal, and C. Loo, "Target tracking in infrared imagery using weighted composite reference function-based decision fusion," IEEE Transactions on Image Processing, vol. 15, pp. 404-410, 2006.
  7. O. Tuzel, F. Porikli, and P. Meer, "Region covariance: A fast descriptor for detection and classification," in Proc. ECCV Conf., 2006, pp. 589–600.
  8. O. Arif and P. A. Vela, "Kernel covariance image region description for object tracking," in 16th IEEE International Conference on Image Processing (ICIP), 2009.
  9. Y. Wu, B. Ma, and P. A. Y. Jia, "Differential tracking with a kernel-based region covariance descriptor," Pattern Analysis and Applications, vol. 18, pp. 45–59, 2015.
  10. W. Forstner and B. Moonen, "A metric for covariance matrices," Technical Report, Dept. of Geodesy and Geoinformatics, Stuttgart University, 1999.
  11. A. Karteek, "The thermal infrared visual object tracking VOT-TIR2015 challenge results," presented at the IEEE International Conference on Computer Vision Workshop (ICCVW), Santiago, 2015.
  12. A. Berg, J. Ahlberg, and M. Felsberg, "A thermal object tracking benchmark," presented at the 12th IEEE International Conference on Advanced Video- and Signal-Based Surveillance, Karlsruhe, Germany, 2015.


Google Scholor ideas Microsoft Academic Search bing Google Scholor


This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.


The authors would like to thank O. Tuzel, F. Porikli, P. Meer, and D. Comaniciu for rewarding discussions. The authors also thank the creators of Linköping Thermal IR (LTIR) data set [6] and OTCBVS benchmark dataset collection for sharing their real-

Related Article