Bio-inspired Metaheuristic based Visual Tracking and Ego-motion Estimation

J. R. Siddiqui, S. Khatibi

2014

Abstract

The problem of robust extraction of ego-motion from a sequence of images for an eye-in-hand camera configuration is addressed. A novel approach toward solving planar template based tracking is proposed which performs a non-linear image alignment and a planar similarity optimization to recover camera transformations from planar regions of a scene. The planar region tracking problem as a motion optimization problem is solved by maximizing the similarity among the planar regions of a scene. The optimization process employs an evolutionary metaheuristic approach in order to address the problem within a large non-linear search space. The proposed method is validated on image sequences with real as well as synthetic image datasets and found to be successful in recovering the ego-motion. A comparative analysis of the proposed method with various other state-of-art methods reveals that the algorithm succeeds in tracking the planar regions robustly and is comparable to the state-of-the art methods. Such an application of evolutionary metaheuristic in solving complex visual navigation problems can provide different perspective and could help in improving already available methods.

References

  1. Aarts, E. & Korst, J., 1988. Simulated annealing and Boltzmann machines. Available at: http:// www.osti.gov/energycitations/product.biblio.jsp?osti_i d=5311236 [Accessed February 16, 2013].
  2. Baik, Y.K. et al., 2013. Geometric Particle Swarm Optimization for Robust Visual Ego-Motion Estimation via Particle Filtering. Image and Vision Computing. Available at: http://www.sciencedirect.com/science/article/pii/S026 2885613000760 [Accessed November 29, 2013].
  3. Baker, S. & Matthews, I., 2004. Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision, 56(3), pp.221-255.
  4. Benhimane, S. & Malis, E., 2007. Homography-based 2d visual tracking and servoing. The International Journal of Robotics Research, 26(7), pp.661-676.
  5. Bjorck, A., 1996. Numerical methods for least squares problems, Society for Industrial Mathematics. Available at: http://www.google.com/ books?hl=sv&lr=&id=ZecsDBMz5-IC&oi=fnd&pg= PA1&dq=+Numerical+methods+for+least+squares+pr oblems&ots=pv2cIqQLF_&sig=kWPokcP6qIVXpuuy LRApvkBUrY4 [Accessed March 6, 2013].
  6. Cobzas, D. & Sturm, P., 2005. 3d ssd tracking with estimated 3d planes. In Computer and Robot Vision, 2005. Proceedings. The 2nd Canadian Conference on. pp. 129-134. Available at: http://ieeexplore.ieee.org/ xpls/abs_all.jsp?arnumber=1443121 [Accessed February 15, 2013].
  7. Davison, A. J., 2003. Real-time simultaneous localisation and mapping with a single camera. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on. pp. 1403-1410. Available at: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1 238654 [Accessed February 16, 2013].
  8. Dowson, N. & Bowden, R., 2006. A unifying framework for mutual information methods for use in non-linear optimisation. Computer Vision-ECCV 2006, pp.365- 378.
  9. Dowson, N. & Bowden, R., 2008. Mutual information for lucas-kanade tracking (milk): An inverse compositional formulation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(1), pp.180-185.
  10. Eade, E. & Drummond, T., 2006. Scalable monocular SLAM. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. pp. 469- 476. Available at: http://ieeexplore.ieee.org/ xpls/abs_all.jsp?arnumber=1640794 [Accessed February 16, 2013].
  11. Goldberg, D. E., 1989. Genetic algorithms in search, optimization, and machine learning. Available at: http://www.citeulike.org/group/712/article/125978 [Accessed February 16, 2013].
  12. Günther, M. & Nissen, V., 2009. A comparison of neighbourhood topologies for staff scheduling with particle swarm optimisation. KI 2009: Advances in Artificial Intelligence, pp.185-192.
  13. Hartley, R. & Zisserman, A., 2000. Multiple view geometry in computer vision, Cambridge Univ Press. Available at: http://journals.cambridge.org/ production/action/cjoGetFulltext?fulltextid=289189 [Accessed February 17, 2013].
  14. Irani, M. & Anandan, P., 2000. About direct methods. Vision Algorithms: Theory and Practice, pp.267-277.
  15. Jurie, F. & Dhome, M., 2002. Hyperplane approximation for template matching. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7), pp.996- 1000.
  16. Kennedy, J. & Eberhart, R., 1995. Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on. pp. 1942-1948. Available at: http://ieeexplore.ieee.org/ xpls/abs_all.jsp?arnumber=488968 [Accessed February 15, 2013].
  17. Klein, G. & Murray, D., 2009. Parallel tracking and mapping on a camera phone. In Mixed and Augmented Reality, 2009. ISMAR 2009. 8th IEEE International Symposium on. pp. 83-86. Available at: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5 336495 [Accessed February 15, 2013].
  18. Lee, G. H. et al., 2010. A benchmarking tool for MAV visual pose estimation. In Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on. pp. 1541-1546. Available at: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5 707339 [Accessed September 1, 2012].
  19. Lucas, B. D. & Kanade, T., 1981. An iterative image registration technique with an application to stereo vision. In Proceedings of the 7th international joint conference on Artificial intelligence. Available at: http://www.ri.cmu.edu/pub_files/pub3/lucas_bruce_d_ 1981_1/lucas_bruce_d_1981_1.ps.gz [Accessed August 30, 2012].
  20. Montemerlo, M. et al., 2002. FastSLAM: A factored solution to the simultaneous localization and mapping problem. In Proceedings of the National conference on Artificial Intelligence. pp. 593-598.
  21. More, J., 1978. The Levenberg-Marquardt algorithm: implementation and theory. Numerical analysis, pp.105-116.
  22. Philippides, A. et al., 2012. How Can Embodiment Simplify the Problem of View-Based Navigation? Biomimetic and Biohybrid Systems, pp.216-227.
  23. Pirchheim, C. & Reitmayr, G., 2011. Homography-based planar mapping and tracking for mobile phones. In pp. 27-36. Available at: http://www.scopus.com/ inward/record.url?eid=2-s2.0- 84055193420&partnerID=40&md5=e5215d84ef1b5a8 ad70c09c10c12de6c.
  24. Scaramuzza, D., Fraundorfer, F. & Siegwart, R., 2009. Real-time monocular visual odometry for on-road vehicles with 1-point ransac. In Robotics and Automation, 2009. ICRA'09. IEEE International Conference on. pp. 4293-4299. Available at: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5 152255 [Accessed September 2, 2012].
  25. Shannon, C. E., 2001. A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), pp.3- 55.
  26. Silveira, G., Malis, E. & Rives, P., 2008. An efficient direct approach to visual SLAM. Robotics, IEEE Transactions on, 24(5), pp.969-979.
  27. Torr, P. & Zisserman, A., 2000. Feature based methods for structure and motion estimation. Vision Algorithms: Theory and Practice, pp.278-294.
  28. Trelea, I. C., 2003. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information processing letters, 85(6), pp.317-325.
  29. Wagner, D., Schmalstieg, D. & Bischof, H., 2009. Multiple target detection and tracking with guaranteed framerates on mobile phones. In Mixed and Augmented Reality, 2009. ISMAR 2009. 8th IEEE International Symposium on. pp. 57-64. Available at: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5 336497 [Accessed February 16, 2013].
  30. Warren, M. et al., 2010. Unaided stereo vision based pose estimation. Available at: http://eprints.qut.edu.au/ 39881/ [Accessed June 22, 2013].
  31. Zhou, H., Green, P. R. & Wallace, A. M., 2009. Estimation of epipolar geometry by linear mixedeffect modelling. Neurocomputing, 72(16-18), pp.3881-3890.
  32. Zhou, H., Wallace, A. M. & Green, P. R., 2009. Efficient tracking and ego-motion recovery using gait analysis. Signal Processing, 89(12), pp.2367-2384.
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Paper Citation


in Harvard Style

R. Siddiqui J. and Khatibi S. (2014). Bio-inspired Metaheuristic based Visual Tracking and Ego-motion Estimation . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 569-579. DOI: 10.5220/0004811105690579


in Bibtex Style

@conference{icpram14,
author={J. R. Siddiqui and S. Khatibi},
title={Bio-inspired Metaheuristic based Visual Tracking and Ego-motion Estimation},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={569-579},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004811105690579},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Bio-inspired Metaheuristic based Visual Tracking and Ego-motion Estimation
SN - 978-989-758-018-5
AU - R. Siddiqui J.
AU - Khatibi S.
PY - 2014
SP - 569
EP - 579
DO - 10.5220/0004811105690579