Surveillance in today's world is a very common issue in computational vision. This activity is present in literature in two different ways: first, as having both camera and objects in motion (Behrad et al. 2000); second, having detection of moving objects by means of one static camera (Lipton et al. 1998). This paper is centered in the last approach, where the interest is to find the movement of objects in images by detecting temporal differences and to define the movement region, which is analyzed by growing region, selecting one region and tracking the object. Once the region is selected, the interest points are determined through a modified corner detector of Harris et al. (1988). A reference data bank is created, to be used in the matching process and determining the characteristic of corresponding points. With these corresponding points, the movement parameters of the region can be estimated and the prediction filter (VSDF) in the tracking cycle initialized. The method that is developed here consists in considering the tracking cycle a matching process by normalized correlation with the help of the prediction filter to adjust the estimated measurements. Thus a method that allows tracking of points of interest in a surveillance region, in a stream of images with significative results to implement appropriate real time algorithms. In this stage of our research Matlab and regular digital cameras were used for prototype design of tools and experimenting. © 2007 Taylor & Francis Group. 5762Azarbayejani, A., Pentland, A., Recursive estimation of motion, structure, and focal length (1995) IEEE Transactions on Pattern Analysis and Machine Intelligence, 17 (6), pp. 562-575Bar-Shalom, Y., Li, X., (1993) Estimation and Tracking: Principles, techniques and software, , Artech House. BostonBehrad, A. Shahrokni, A. Motamedi, S.A. and Madani, K. 2000. A Robust Vision-Based Moving Target Detection and Tracking System. In madani@univ-paris12.frBerger, M-O, Wrobel-Dautcourt, B. Petitjean, S. and Simon, G. 1999. Mixing synthetic and video images of an outdoor urban environment. Machine Vision and Applications. 1999, (11), pp. 145-159, NovBroida, T., Chandrashekhar, S., Chellappa, R., Recursive 3D Motion Estimation from a Monocular Image Sequence (1990) IEEE Transactions on Aerospace and Electronic Systems, 26 (4), pp. 639-656. , July. ppBrown, R.G., Hwang, Y.C., (1997) Introduction to Random Signals and Applied Kalman Filtering, , 3rd Edition, John Wiley & Sins, IncCollins, L., Kanade, F., Duggins, T., Tolliver, E., Hasegawa, A System for Video Surveillance and Monitoring: VSAM Final Report, (2000), Technical report CMU-RI-TR-00-12, Robotics Institute, Carnegie Mellon University, MayForstner, W., A Framework for Low Level Feature Extraction (1994) Lecture Notes in Computer Science, 802, pp. 383-394. , Computer Vision-ECCV'94. ppFusiello, A., Tommasini, T., Trucco, E., Roberto, V., Making Good Features to Track Better (1998) Proceeding of the IEEE Conference on Computer Vision and Pattern Recog, pp. 178-183. , Santa Barbara, CAHaritaoglu, I. Harwood, D. and Davis, L.S. 1998. W4 Who? When? Where? What? A Real Time System for Detecting and Tracking People. In 3rd International Conference on Face and Gesture Recognition. Nara. Japan, pp: 222-227Harris, C. and Stephens, M. 1988. A Combined Corner and Edge Detector. In Proceeding 4th Alvey Vision Conference (AVC88) pp. 147-151Hartley, R.I., In Defence of the 8-point Algorithm (1995) Proceeding of the IEEE International Conference on Computer VisionHartley, R.I., Kruppa's Equations Derived from Fundamental Matrix (1997) IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (2), pp. 133-135. , FebJaynes, C., Fast Feature Extraction of Mechanical Parts in Motion, (1999), Technical Report, Department of Computer Science. University of KentuckyKanade, T., Tomassi, C., Detection and Tracking of point features (1991), Technical Report CMU-CS-91-132. Carnegie Mellon University, AprilKoller, D. Klinker, G. Rose, E. Breen, D Whitaker, R. Tuceryan M. 1998. Real-time Vision-Based Camera Tracking for Augmented Reality Applications. Tec. Rep. California Inst. Of Technology. e-mail dieter. koller@autodesk.comLipton, A. J. Fujiyoshi, H. and Patil, R. S. 1998. Moving Target Classification and Tracking from Real-Time Video. In proc. IEEE Workshop on Applications of Computer Vision (WACV). Princeton NJ, October, pp. 8-14McLauchlan, P., Murray, D., A Unifying Framework for Structure and Motion recovery From Image Sequence (1995) Proc. 5th Int'l Conf. On Computer Vision, pp. 314-320. , Boston, pp, JuneMcLauchlan, P., The Variable State Dimension Filter (1999) Technical Report VSSP, , 4/99. University of Surrey, Dept. of Electrical Engineering, NovPilu, M., A Direct Method for Stereo Correspondence based on Singular Value Decomsition (1997) Proc. CVPR, pp. 261-266. , 97, ppRavela, S., Draper, B., Lim, J., Weiss, R., Tracking Object Motion Across Aspect Changes for Augmented Reality (1996) Proc. ARPA Image Understanding Workshop, , Palm Spring, CASchmid, C., Mohr, R., Bauckhage, C., Evaluation of Interest Point Detectors (2000) International Journal of Computer Vision, 37 (2), pp. 151-172Shapiro, L., Stockman, G., (2000) Computer VisionSoatto, S., Frezza, R., Perona, P., Motion Estimation via Dynamic Vision, (1994) Technical Report CIT-CDS, , 94-004, California Institute of TechnologyTrucco, E., Verri, A., (1998) Introductory techniques for 3D Computer Vision, , Prentice Hall. NJTuceryan, M., Greer, D., Whitaker, R., Breen, D., Crapton, C., Ahlers, K., Calibration Requirements and Procedures for a Monitor-Based Augmented Reality System (1995) IEEE Trans. On Visualization and Computer Graphics, 1 (3), pp. 255-273. , Sep. ppWelch, G., Bishop, G., Kalman Filter (2001) SIGGRAPH 2001, , Los Angeles, CA, AugustWeng, J., Cohen, P., Herniou, Camera Calibration with Distortion Models Accuracy Evaluation (1992) IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 (10), pp. 965-980Wren, C., Azarbayejani, A., Darrell, T., Pentland, A., Pfinder: Real-time tracking of the human body (1997) IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (7), pp. 780-785Zhang, Z., Deriche, R., Faugeras, O., Luong, Q., A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry, (1996) INRIA Research Report, , 2273, May