We present a Kalman tracking algorithm that can track a number of very small, low contrast objects through an image sequence taken from a static camera. The issues that we have addressed to achieve this are twofold. Firstly, the

A critical issue in Automatic Target Recognition is the detection and identification of low contrast targets in Forward Looking Infra-Red (FLIR) images at sufficiently long ranges. In this paper, we describe a technique based on determining the best magnification to zoom in onto a target object while it is still at a long range. We model

The hypothesis verification stage of the traditional image processing approach, consisting of low, medium, and high level processing, will suffer if the set of low level features extracted are of poor quality. We investigate

We present a Kalman tracking algorithm that can track a number of very small, low contrast objects through an image sequence taken from a static camera. The issues that we have addressed to achieve this are twofold. Firstly, the detection of small objects comprising a few pixels only, mov-ing slowly in the image, and secondly, tracking of multiple small targets even though they may be lost either through occlusion or in noisy signal. The ap-proach uses a combination of wavelet filtering for detection with an interest operator for testing multiple target hypotheses based within the framework of a Kalman tracker. We demonstrate the robustness of the approach to oc-clusion and for multiple targets. 1

A review on strategies for recognizing natural objects in colour images of outdoor scenes

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