The week's pick
Random Articles
Reseach Article
2.5D Feature Tracking and 3D Motion Modeling
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 64 - Number 5 |
| Year of Publication: 2013 |
| Authors: Mozhdeh Shahbazi |
10.5120/10634-5375
|
Mozhdeh Shahbazi . 2.5D Feature Tracking and 3D Motion Modeling. International Journal of Computer Applications. 64, 5 ( February 2013), 43-50. DOI=10.5120/10634-5375
Abstract
Image-based tracking of objects is becoming an important area of research within computer vision and image processing community. However, there are still challenges with regard to robustness of the algorithms. This paper explains an algorithm to track the pre-defined objects within stereo videos (image sequences) in a condition where cameras are fixed and objects are moving. The tracking technique used in this research, applies the intensity-based least squares matching (LSM) to find the correspondent targets in successive frames. Unlike ordinary correlation-based registration methods, LSM takes both geometric and radiometric variations of images into account, succeeding at sub-pixel scale feature tracking. The proposed algorithm combines three dimensional updated object constraints with adaptive two dimensional LSM to ensure the robustness and convergence to optimum solution. While tracking the features in stereo images, photogrammetric techniques are applied to extract the coordinates of the features in object space which result in detecting the 3D trajectory of the features. The average tracking error is about 0. 11 pixel at x-direction and 0. 15 pixel at y-direction. The 3D motion vectors are modeled by mean magnitude precision of 0. 65 millimeter and orientation precision of 0. 27 degree.
References
- Tissainayagam, P. , and Suter, D. 2005. Object tracking in image sequences using point features. Pattern Recognition, 38(1), 105-113.
- Koller, D. , Weber, J. , and Malik, J. 1994. Robust multiple car trackingwith occlusion reasoning. ECCV 94, 189–196.
- Hsu, L. Y. , and Loew, M. H. 2001. Fully automatic 3D feature-based registration of multi-modality medical images. Image and Vision Computing, 19(1), 75-85.
- Tao, C. V. , Chapman, M. A. , and Chaplin, B. A. 2001. Automated processing of mobile mapping image sequences. ISPRS journal of Photogrammetry and Remote Sensing, 55(5), 330-346.
- Blake, A, and Isard, M. 1998. Active Contours, Springer, Berlin.
- Pateraki, M. , Baltzakis, H. , Kondaxakis, P. , and Trahanias, P. 2009. Tracking of facial features to support human-robot interaction. In Proceedings of IEEE International Conference on Robotics and Automation.
- Jiang, N. 2009. The extraction, restoration and tracking of image features. Doctoral dissertation, Arizona State University, Arizona.
- Smith, S. M. and Brady, J. M. 1995. Real-time motion segmentation and shape tracking. Transactions of IEEE on Pattern Matching and Machine Intelligence, 17(8), 814-820.
- Previtali, M. , Barazzetti, L. , Scaioni, M. , and Tian, Y. 2011. An automatic multi-image procedure for accurate 3D object reconstruction. In Proceedings of IEEE Congress on Image and Signal Processing.
- Shahbazi, M. , and Motagh, M. 2012. Improved Interferometric Synthetic Aperture Radar processing via advanced co-registration and phase correction techniques. In Proceedings of IEEE Conference on Intelligent Data Understanding.
- Akca, D. 2007. Matching of 3D surfaces and their intensities. ISPRS Journal of Photogrammetry and Remote Sensing, 62(2), 112-121.
- Shin, D. , and Muller, J. P. 2012. Progressively weighted affine adaptive correlation matching for quasi-dense 3D reconstruction. Pattern Recognition. 45(1), 3795-3809.
- Rosenholm, D. 1987. Least squares matching method:some experimental results. The Photogrammetric Record, 12(70), 493-512.
- Rosenholm, D. 1987. Empirical investigation of optimal window size using the least squares image matching method. Photogrammetria, 42(3), 113-125.
- Luhmann, T. , Robson, S. , Kyle, S. and Harley, I. 2007. Close range photogrammetry: principles, techniques and applications. John Wiley & Sons, UK.
- Shahbazi, M. , Homayouni, S. , Saadatseresht, M. , and Sattari, M. 2011. Range camera self-calibration based on integrated bundle adjustment via joint setup with a 2D digital camera. Sensors, 11(9), 8721-8740.
- Michaiel, E. M. 1976. Observations and least squares. IEP-A Dun-Donelley, USA.
- Wolf, P. R. , and Dewitt, B. A. 2000. Elements of Photogrammetry: with applications in GIS. McGraw-Hill, USA.
- Gruen, A. 1985. Adaptive least squares correlation: a powerful image matching technique. South African Journal of Photogrammetry, Remote Sensing and Cartography, 14(3), 175-187.
- Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66.
- Gonzalez, R. C. , Woods, R. E. , and Eddins, S. L. 2004. Digital image processing using MATLAB. Pearson Education, India
Index Terms
Keywords