Abstract
We present a novel algorithm to achieve automatic detection and positioning of changes for monitoring systems in complex environments. The aim is to efficiently detect changes of unknown dimensions, shapes and velocity and to position them in a sequence of images. The practicality of the algorithm is simplified by the use of different decision rules in a multistage test for different purposes. These decision rules identify the changes and number of parts, as well as the position and its optimal pick-up points for each individual part. A lighting compensation method is embedded to maintain a constant lighting environment and therefore the error rate can be reduced. Experimental results on a variety of image sequences show that the proposed algorithm is effective and efficient, regardless of the irregularity and number of changes.
Similar content being viewed by others
References
Abou-El-Ela, M. S. and El-Amroussy, H.: A machine vision system for the recognition and positioning of two-dimensional partially occluded objects, in: Proc. MELECON'96, 1996, pp. 1087–1092.
Blostein, S. D. and Huang, T. S.: Detection small, moving objects in image sequences using sequential hypothesis testing, IEEE Trans. Signal Process. 39(2) (July 1991), 1611–1629.
Canus, C. and Vehel, J. L.: Change detection in sequences of images by multifractal analysis, in: Proc. IGARSS'96, Vol. 3, 1996, pp. 1609–1611.
Caves, R. G. and Quegan, S.: Segmentation based change detection in ERS-1 SAR images, in: Proc. IGARSS'94, Vol. 94, 1994, pp. 2149–2151.
Dugad, R., Ratakonda, K., and Ahuja, N.: Robust video shot change detection, in: Proc. of IEEE 2nd Workshop on Multimedia Signal Processing, 1998, pp. 376–381.
Fung, T.: An assessment of TM imagery for land-cover change detection, IEEE Trans. Geosci. Remote Sensing 28(12) (1990), 681–684.
Hsu, Y. Z., Nagel, H. H., and Rekers, G.: New likelihood test methods for change detection in image sequences, Computer Vision Graphics Image Process.26 (1984), 73–106.
Jain, R., Martin, W. N., and Aggarwal, J. K.: Segmentation through the detection of changes due to motion, Computer Graphics Image Process. 11 (1979), 13–34.
Jain, Z.-S. and Chau, Y. A.: Optimum multisensor data fusion for image change detection, IEEE Trans. Systems Man Cybernet.25(9) (1995), 1340–1347.
Paragios, N. and Tziritas, G.: Detection and location of moving objects using deterministic relaxation algorithms, in: Proc. 11th Internat. Conf. on Pattern Recognition, Vol. 1, 1996, pp. 201–205.
Singh, A.: Digital change detection techniques using remote-sensed data, Internat. J. Remote Sensing 10(6) (1989), 989–1003.
Trucco, E., Umasuthan, M., Wallace, A. M., and Roberto, V.: Model-based planning of optimal sensor placements for inspection, IEEE Trans. Robotics Automat. 132 (April 1997), 182–194.
Yakimovsky, Y.: Boundary and object detection in real world images, J. ACM 23 (1976), 13–34.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Yang, CH., Chung, PC. Knowledge-based Automatic Change Detection and Positioning System for Complex Heterogeneous Environments. Journal of Intelligent and Robotic Systems 33, 85–98 (2002). https://doi.org/10.1023/A:1014436412732
Issue Date:
DOI: https://doi.org/10.1023/A:1014436412732