Decision fusion algorithm for target tracking in infrared imagery

Abstract

We propose a novel decision fusion algorithm for target tracking in forward-looking infrared (FLIR) image sequences recorded from an airborne platform. The algorithm allows the fusion of complementary ego-motion compensation and tracking algorithms to estimate the position of the target in the current frame among a sequence of frames of FLIR imagery. We identified three modes that contribute to the failure of the tracking system: (1) the sensor ego-motion failure mode, which causes the movement of the target beyond the operational limits of the tracking stage; (2) the tracking failure mode, which occurs when the tracking algorithm fails to determine the correct location of the target in the new frame; (3) the reference-image distortion failure mode, which happens when the reference image accumulates walkoff error, especially when the target is changing in size, shape, or orientation from frame to frame. The strategy in our design is to prevent these failure modes from producing tracking failures. The overall performance of the algorithm is guaranteed to be much better than any individual tracking algorithm used in the fusion. One important aspect of the proposed algorithm is its recoverability: the ability to recover following a failure at a certain frame. The experiments performed on Army Missile Command AMCOM FLIR data set verify the robustness of the algorithm.