Browse > Article

Reliable Measurement Selection for The Small Target Detection and Tracking in The IR Scanning Images  

Yang, Yu-Kyung (ADD)
Kim, Sung-Ho (ADD)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.11, no.1, 2008 , pp. 75-84 More about this Journal
Abstract
A new automatic small target detection and tracking algorithm for the real-time IR surveillance system is presented. The automatic target detection and tracking algorithm of the real-time systems, requires low complexity and robust tracking performance in the cluttered environment. Linear-array and parallel-scan IR systems usually suffer from severe scan noise caused by the detector non-uniformity. After the spatial filtering and thresholding, this scan noise still remains as high amplitude clutter which degrades the target detection rate and tracking performance. In this paper, we propose a new feature which consists of area and validity information of a measurement. By adopting this feature to the measurements selection and track confirmation, we can increase the target detection rate and reduce both the track loss rate and false track rate. From the experimental results, we can validate the feasibility of the proposed method in the noisy IR images.
Keywords
Small Target; Detection; Tracking; Measurement Selection; IRST;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Tom, V. T., et al., "Morphology-based Algorithm for Point Target Detection in Infrared Backgrounds", SPIE, Vol. 1954, pp. 2-11, 1993
2 Li, X. R., "Tracking in Clutter with Strongest Neighbor Measurements- Part I : Theoretical Analysis", IEEE Transactions on Automatic Control, Vol. 43, No. 11, 1998
3 Kirubarajan, T. and Bar-Shalom, Y., "Probabilistic Data Association Techniques for Target Tracking in Clutter", IEEE, Vol. 92, No. 3, 2004
4 Arulampalam, M. S., et al., "A Tutorial on Particle Filters for Online Nonlinear/Non- Gaussian Bayesian Tracking", IEEE Transactions on Signal Processing, Vol. 50, No. 2, 2002
5 Barnett, J. T., et al., "Nonlinear-morphological Processors for Point-Target Detection Versus An Adaptive Linear Spatial Filter : A Performance Comparison", SPIE, Vol. 1954, pp. 12-24, 1993
6 Schmidt, W. A. C., "Modified Matched Filter for Cloud Clutter Suppression", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 6, pp. 594-600, 1990   DOI   ScienceOn
7 Bar-Shalom, Y. and Fortmann, T. E., Tracking and Data Association, Academic Press, pp. 56-122, 1998
8 Watson, G. A. and Blair, W. D., "IMM Algorithm for Tracking Targets that Maneuver Through Coordinated Turns", Proc. SPIE, Vol. 1698, pp. 236-247, 1992
9 LI, X. R. and Bar-Shalom, Y., "Tracking in Clutter With Nearest Neighbor Filters : Analysis and Performance", IEEE Transactions on Aerospace and Electronic Systems, Vol. 32, No. 3, 1996
10 Warren, R. C., "The Performance of Small Support Spatial and Temporal Filters for Dim Point Target Detection in IR Image Sequences", DSTO-TR-1282, DSTO Aeronautical and Maritime Research Laboratory, 2002