Infrared image sequence complexity analysis based on multi-attribute decision making

To analyse the influences of infrared sequence complexity on the target tracking performance, the infrared sequence complexity evaluation had been modeled as a multi-attribute decision making problem. The each frame complexity of the infrared sequence had been evaluated with seven image metrics based on the modified technique for order preference by similarity to ideal solution method and entropy weights. The whole infrared image sequence complexity had been evaluated with three metrics based on weighted summation method and entropy weights. The normalized correlation template matching algorithm, basic mean shift algorithm, and the variance ratio algorithm had been used to implement tracking experiments. Infrared sequences with different complexity had beed used to validate the effectiveness of the presented infrared sequence evaluation method. The experiments showed that: the proposed infrared sequence complexity evaluation solution could truly indicate the differences of the tracking task difficulties for diverse infrared sequences, there was strong correlation with the tracking performance, and could accurately reflect the major influencing factors for target tracking task.