Abstract
Template matching is an important technique used for object tracking. It aims at finding a given pattern within a frame sequence. Pearson’s Correlation Coefficient (PCC) is widely used to quantify the degree of similarity of two images. This coefficient is computed for each image pixel. This entails a computationally very expensive process. In this work, aiming at accelerating this process, we propose to implement the template matching as an embedded co-design system . In order to reduce the processing time, a dedicated co-processor, which is responsible for performing the PCC computation is designed and implemented. In this chapter, two techniques of computational intelligence are evaluated to improve the search for the maximum correlation point of the image and the used template: Particle Swarm Optimization (PSO) was better than Genetic Algorithms (GA). Thus, in the proposed design, the former is implemented in software and is run by an embedded general purpose processor, while the dedicated co-processor executes all the computation regarding required PCCs. The performance results show that the designed system achieves real-time requirements as needed in real-word applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahuja, K., Tuli, P.: Object recognition by template matching using correlations and phase angle method. Int. J. Adv. Res. Comput. Commun. Eng. 2(3), 1368–1373 (2013)
Ali, A., Kausar, H., Khan, M.I.: Automatic visual tracking and firing system for anti aircraft machine gun. In: 6th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 253–257. IEEE, Piscataway (2009)
Avnet: PicoZed 7Z015 / 7Z030 System-On Module Hardware User Guide, version 1.3 (2015)
Benfold, B., Reid, I.: Stable multi-target tracking in real-time surveillance video. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3457–3464. IEEE, Piscataway (2011)
Choi, H., Kim, Y.: UAV guidance using a monocular-vision sensor for aerial target tracking. Control Eng. Pract. 22, 10–19 (2014)
Collins, R., Zhou, X., Teh, S.K.: An open source tracking testbed and evaluation web site. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, vol. 2, p. 35 (2005)
Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley, West Sussex (2006)
Forlenza, L., Fasano, G., Accardo, D., Moccia, A.: Flight performance analysis of an image processing algorithm for integrated sense-and-avoid systems. Int. J. Aerosp. Eng. 2012, 1–8 (2012)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Network, vol. 4, pp. 1942–1948. EUA (1995)
Kosheleva, O.: Babylonian method of computing the square root: justifications based on fuzzy techniques and on computational complexity. In: Fuzzy Information Processing Society, NAFIPS, pp. 1–6. IEEE, Piscataway (2009)
Mahalakshmi, T., Muthaiah, R., Swaminathan, P., Nadu, T.: Review article: an overview of template matching technique in image processing. Res. J. Appl. Sci. Eng. Technol. 4(24), 5469–5473 (2012)
Narayana, M.: Automatic tracking of moving objects in video for surveillance applications. Ph.D. Thesis, University of Kansas (2007)
Nedjah, N., Mourelle, L.M.: Co-design for System Acceleration: A Quantitative Approach. Springer Science & Business Media, Berlin (2007)
Nixon, M.S., Aguado, A.S.: Feature Extraction and Image Processing, 1st edn. Academic, Great Britain (2002)
Olson, T.L., Sanford, C.W.: Real-time multistage IR image-based tracker. In: AeroSense’99, pp. 226–233. International Society for Optics and Photonics, Bellingham (1999)
SensorToImage: SVDK Hardware User Guide, revision 1.1 (2015)
Sharma, P., Kaur, M.: Classification in pattern recognition: a review. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(4), 1–3 (2013)
Tavares, Y.M., Nedjah, N., Mourelle, L.M.: Embedded Implementation of template matching using correlation and particle swarm optimization. In: International Conference on Computational Science and Its Applications, pp. 530–539. Springer, Beijing (2016)
Tavares, Y.M., Nedjah, N., Mourelle, L.M.: Tracking patterns with particle swarm optimization and genetic algorithms. Int. J. Swarm Intell. Res. 8(2), 34–49 (2017)
Xilinx: UG585 Zynq-7000 AP SoC Technical Reference Manual, version 1.10 (2015)
YouTube: Rafale - High Technology Hunting Plane (Brazil) (in Portuguese) (2015)
Acknowledgements
Y. M. Tavares acknowledges the Brazilian Navy for the support given during the development of his research work. We are also grateful to FAPERJ (Fundação Carlos Chagas Filho de Amparo Ă Pesquisa do Estado do Rio de Janeiro, http://www.faperj.br) and CNPq (Conselho Nacional de Desenvolvimento CientĂfico e TecnolĂłgico, http://www.cnpq.br) for their continuous financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Tavares, Y.M., Nedjah, N., de Macedo Mourelle, L. (2019). Co-design System for Tracking Targets Using Template Matching. In: Platt, G., Yang, XS., Silva Neto, A. (eds) Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-96433-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-96433-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96432-4
Online ISBN: 978-3-319-96433-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)