# Estimation of moving information for tracking of moving objects

- 53 Downloads

## Abstract

Tracking of moving objects within video streams is a complex and time-consuming process. Large number of moving objects increases the time for computation of tracking the moving objects. Because of large computations, there are real-time processing problems in tracking of moving objects. Also, the change of environment causes errors in estimation of tracking information. In this paper, we present a new method for tracking of moving objects using optical flow motion analysis. Optical flow represents an important family of visual information processing techniques in computer vision. Segmenting an optical flow field into coherent motion groups and estimating each underlying motion are very challenging tasks when the optical flow field is projected from a scene of several moving objects independently. The problem is further complicated if the optical flow data are noisy and partially incorrect. Optical flow estimation based on regulation method is an iterative method, which is very sensitive to the noisy data. So we used the Combinatorial Hough Transform (CHT) and Voting Accumulation for finding the optimal constraint lines. To decrease the operation time, we used logical operations. Optical flow vectors of moving objects are extracted, and the moving information of objects is computed from the extracted optical flow vectors. The simulation results on the noisy test images show that the proposed method finds better flow vectors and more correctly estimates the moving information of objects in the real time video streams.

## Key Words

Object Tracking Optical Flow Hough Transform Voting## Preview

Unable to display preview. Download preview PDF.

## References

- Adiv, G., 1989, “Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Field,”
*IEEE Trans. Pattern Analysis Mach. Intell.*11 (5), pp. 477–489.CrossRefGoogle Scholar - Ben-Tzvi, D. and Sandler, M., 1990, “A Combinatorial Hough Transform,”
*Pattern Recognition, Lett.*11, pp. 167–174.MATHCrossRefGoogle Scholar - Black, M. J., 1992, “Combining Intensity and Motion for Incremental Segmentation and Tracking over Long Image Sequence,”
*Proc. ECCV’92*, pp. 485–493.Google Scholar - Broida, T. J. and Chellappa, R., 1989, “Experiments and Uniqueness Results on Object Structure and Kinematics from a Sequence of Monocular Images,”
*Proc. IEEE Workshop on Visual Motion*, Irvine, California, U. S. A, pp. 21–30.Google Scholar - Burt, P. J., et al., 1989, “Object Tracking with a Moving Camera,”
*Proc. IEEE Workshop on Visual Motion*, Irvine, California, U. S. A., pp. 2–12.Google Scholar - Cafforio, C. and Rocca, F., 1979, “Tracking Moving Objects in Television Images,”
*Signal Process*. 1, pp. 133–140.CrossRefGoogle Scholar - Campani, M. and Verri, A. 1990, “Computing Optical Flow from an Overconstrained System of Linear Algebraic Equations,”
*Proc. 3rd IEEE Int. Conf. on Computer Vision*, pp. 22–26.Google Scholar - Del Bimbo, A. and Nesi, A., 1993, “Real-Time Optical Flow Estimation,”
*in Proc. 1993 IEEE Systems, Man and Cybernetics Conf., (Le Touquet, France)*, pp. 17–20.Google Scholar - Etoh, M. et. al., 1993, “Segmentation and 2D Motion Estimate by Region Fragments,”
*Proc. 4th Int. Conf. Computer Vision*, pp. 192–199.Google Scholar - Gemen, S. et. al., 1985, “Stochastic Relaxation, Gibbs Distribution, and Bayesian Restoration of Images,”
*IEEE Trans PAMI*. 6 (6), pp. 721–741.Google Scholar - Han, M. C., Hong, K. S. and Kim, J. K., 1996, “The Position Measurement of a Body Using 2D Vision Sensors.”
*’96 Proceedings of the KSME Spring Annual Meeting (A)*, pp. 270–275.Google Scholar - Horn, B. K. P. and Schunck, B. G., 1981, “Determining Optical Flow,”
*Artif. Intell.*17, pp. 185–203.CrossRefGoogle Scholar - Leavers, V., Ben-Tzvi, D. and Sandler, M., 1989, “A Dynamic Combinatorial Hough Transform for Straight Lines and Circles,”
*Proc. Alvey Vision Conf. Manchester*, U. K.Google Scholar - Nagao, K. et. al., 1993, “Detecting Contours in Image Sequences,”
*IEICE Trans. Information and System*. E76-D(10), pp. 1162–1173.Google Scholar - Nagel, H. H., 1983, “Displacement Vectors Derived from Second-Order Intensity Variations in Image Sequences,”
*Computer Vision Graphics Image Process*. 21, pp. 85–117.CrossRefGoogle Scholar - Nagel, H. H. 1989, “On a Constraint Equation for the Estimation of Displacement Rates in Image Sequences,”
*IEEE Trans. Pattern Analysis Mach. Intell.*11 (1), pp. 13–30.MATHCrossRefGoogle Scholar - Nagel, H. H. and Enkelmann, W. 1984, “Towards the Estimation of Displacement Vector Fields by ‘Oriented Smoothness’ Constraints,”
*Proc. 7th IEEE Int. Conf. on Pattern Recognition*, pp. 6–8.Google Scholar - Nelson, R. C. and Aloimonos, J., 1989, “Obstacle Avoidance Using Field Divergence,”
*IEEE Trans. Pattern Analysis Mach. Intell.*11 (10), pp. 1102–1106.CrossRefGoogle Scholar - Nesi, P., Delbimbo, A. and Sanz, J. L., 1991, “Multiconstraints-Based Optical Flow Estimation and Segmentation,”
*Int. Workshop on Computer Architecture for Machine Perception*, Paris, pp. 419–426.Google Scholar - Prazdny, K., 1983, “On the Information in Optical Flow,”
*Computer Vision Graphics Image Process*. 23, pp. 239–259.CrossRefGoogle Scholar - Schunck, B. G., 1989, “Image Flow Segmentation and Estimation by Constraints Line and Clustering,”
*IEEE Trans. Pattern Analysis Mach. Intell.*11 (10), pp. 1010–1027.CrossRefGoogle Scholar - Subbarao, M., 1990, “Bounds on Time-to-Collision and Rotation Component from First-Order Derivatives of Image Flow,”
*Computer Vision Graphics Image Process*. 50, pp. 329–341.CrossRefGoogle Scholar - Thompson, W. B., 1980, “Combining Motion and Contrast for Segmentation,”
*IEEE Trans. PAMI*, 2 (6), pp. 543–549.Google Scholar - Verri, A. and Poggio, T., 1989, “Motion Field and Optical Flow: Qualitative Properties,”
*IEEE Trans. Pattern Analysis Mach. Intell.*11 (5), pp. 490–498.CrossRefGoogle Scholar