Proceedings Article | 5 August 2009
Proc. SPIE. 7383, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications
KEYWORDS: Target detection, Infrared search and track, Infrared imaging, Optical filters, Matrices, Particles, Particle filters, Infrared radiation, Electronic filtering, Nonlinear filtering
Robust infrared target tracking is an important and challenging research topic in many military and security applications,
such as infrared imaging guidance, infrared reconnaissance, scene surveillance, etc. To effectively tackle the nonlinear
and non-Gaussian state estimation problems, particle filtering is introduced to construct the theory framework of infrared
target tracking. Under this framework, the observation probabilistic model is one of main factors for infrared targets
tracking performance. In order to improve the tracking performance, covariance matrices are introduced to represent
infrared targets with the multi-features. The observation probabilistic model can be constructed by computing the
distance between the reference target's and the target samples' covariance matrix. Because the covariance matrix
provides a natural tool for integrating multiple features, and is scale and illumination independent, target representation
with covariance matrices can hold strong discriminating ability and robustness. Two experimental results demonstrate
the proposed method is effective and robust for different infrared target tracking, such as the sensor ego-motion scene,
and the sea-clutter scene.
