Object Tracking System Based on Artificial Vision Algorithms

Abstract:

This document describes the architecture of a tracking system with two degrees of freedom (pan and tilt), endowed with artificial vision to follow the path of a moving object. The mechanism with a fixed base was designed to cover lateral and vertical ranges of movement, similar to the visual field in humans, limiting its depth by the resolution of the camera. The object that defines the motion path presents color uniformity across its surface, becoming the main feature in which the recognition and tracking algorithm were based. The tracking is performed by reducing the error between the object position and the reference axis of the camera. Several tests were carried out to evaluate the control and visual systems and to illustrate the behavior of the proposed methods.

• Periodical: Applied Mechanics and Materials (Volumes 713-715)
• Pages: 420-423
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.713-715.420
• Online since: January 2015
• Authors: Mauricio M. Mauledoux*, Carlos Hernandez, Crhistian C.G. Segura, Oscar F. Aviles
• Keywords: Artificial Vision, PID Control, Tracking

References

[1] Pawlowski, P. and Borowczyk, K. and Marciniak, T. and Dabrowski, A., Real-time object tracking using motorized camera, in Signal Processing Algorithms, Architectures, Arrangements, and Applications Conference Proceedings (SPA), 2009 (, 2009), pp.110-115.

DOI: 10.1109/spa.2007.5903293

[2] Chakraborty, D. and Patra, D., Real time object tracking based on segmentation and Kernel based method, in Industrial and Information Systems (ICIIS), 2010 International Conference on (, 2010), pp.426-429.

DOI: 10.1109/iciinfs.2010.5578665

[3] Junqiu Wang and Yagi, Y., Integrating Color and ShapeTexture Features for Adaptive Real-Time Object Tracking, Image Processing, IEEE Transactions on 17, 2 (2008), pp.235-240.

DOI: 10.1109/tip.2007.914150

[4] C.H. Kung, C.M. Kung, and J. P. Wang. High performance real-time object detecting and tracking system for multiple moving targets. In Instrumentation and Measurement Technology Conference Proceedings, 2007. IMTC 2007. IEEE, pages 1–4, May (2007).

DOI: 10.1109/imtc.2007.379185

[5] Alan M. McIvor. Background subtraction techniques. (2002).

[6] M. Marengoni and D. Stringhini. High level computer vision using OpenCV. In Graphics, Patterns and Images Tutorials (SIBGRAPI-T), 2011 24th SIBGRAPI Conference on, pages 11–24, (2011).

DOI: 10.1109/sibgrapi-t.2011.11

[7] Guishan Xiang, Real-Time Follow-Up Tracking Fast Moving Object with an Active Camera", in Image and Signal Processing, 2009. CISP , 09. 2nd International Congress on (2009), pp.1-4.

DOI: 10.1109/cisp.2009.5303457