A method of detecting dim targets in highly-cluttered time-varying image sequences is presented, where reliable clutter rejection is achieved by calibrating the multivariate statistics of a small number of generic space-time filters. The targets have sufficiently low SCR that a track-before-detect method is required. For targets where there is little prior information on velocity, a large number of filters is generally required to achieve a high response relative to the background. In the method described here, instead of applying thresholds to individual filters, joint filter statistics are used to estimate conditional threshold exceedance probabilities. A smaller number of more generic filters are applied, which are not finely tuned to targets but which characterise aspects of both targets and clutter. Potential targets are cued based on a non-parametric estimate of the probability of occurrence of similar clutter. Constant fal se alarm rates are inherent in the method. The method is demonstrated on examples of real forward-looking imagery of the sea surface, where glint is a source of strong clutter. Dim targets are distinguished from clutter by using the joint statistics of three variables: a constant-intensity blob filter, a filter tuned to sea glint flashes, and the vertical image co-ordinate.

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This paper is concerned with the detection of dim targets in cluttered image sequences. It is an extension of our previous work [7] in which we viewed target detection as an outlier detection problem. In that work the background was modelled by a uni-modal Gaussian. In this paper a Gaussian mixture-model is used to describe the background in which the the number of components is automatically selected. As an outlier does not automatically imply a target, a final stage has been added in which all points below a set density function value are passed to a support vector classifier to be identified as a target or background. This system is compared favourably to a baseline technique [12].