Target tracking in airborne forward looking infrared imagery

摘要

In this paper, we propose a robust approach for tracking targets in forward looking infrared (FLIR) imagery taken from an airborne moving platform. First, the targets are detected using fuzzy clustering, edge fusion and local texture energy. The position and the size of the detected targets are then used to initialize the tracking algorithm. For each detected target, intensity and local standard deviation distributions are computed, and tracking is performed by computing the mean-shift vector that minimizes the distance between the kernel distribution for the target in the current frame and the model. In cases when the ego-motion of the sensor causes the target to move more than the operational limits of the tracking module, we perform a multi-resolution global motion compensation using the Gabor responses of the consecutive frames. The decision whether to compensate the sensor ego-motion is based on the distance measure computed from the likelihood of target and candidate distributions. To overcome the problems related to the changes in the target feature distributions, we automatically update the target model. Selection of the new target model is based on the same distance measure that is used for motion compensation. The experiments performed on the AMCOM FLIR data set show the robustness of the proposed method, which combines automatic model update and global motion compensation into one framework.