CAJ | 학술논문

　　针对复杂背景下的运动目标跟踪特征选择问题，提出了一种基于粒子群优化的目标跟踪特征选择算法。假设具有目标与背景间最好可分离性的特征为最好的跟踪特征。通过构建目标与背景的图像特征分布方差的比值函数作为衡量目标与背景间的可分离性判据。使用粒子群优化算法优化不同的特征组合实时获取最优的目标跟踪特征。为验证该算法的有效性，将选择的最优特征与一种基于核的跟踪算法相结合进行跟踪实验。实验结果表明，算法能有效提高传统基于核的跟踪算法对于复杂场景下的运动目标跟踪的鲁棒性与准确性。
　　침대복잡배경하적운동목표근종특정선택문제，제출료일충기우입자군우화적목표근종특정선택산법。가설구유목표여배경간최호가분리성적특정위최호적근종특정。통과구건목표여배경적도상특정분포방차적비치함수작위형량목표여배경간적가분리성판거。사용입자군우화산법우화불동적특정조합실시획취최우적목표근종특정。위험증해산법적유효성，장선택적최우특정여일충기우핵적근종산법상결합진행근종실험。실험결과표명，산법능유효제고전통기우핵적근종산법대우복잡장경하적운동목표근종적로봉성여준학성。
In this paper a novel tracking feature selection method is presented. It assumes the features that have best distinctive-ness between object and background are also best for tracking the object. A two-class variance ratio is employed to measure the distinctiveness. Particle swarm optimization algorithm is used to optimize the different features combination to generate the best tracking feature. To demonstrate the proposed method, selected feature is combined with kernel-based tracking method. Experi-mental results show that the proposed method can robustly track moving object in low discriminately background scenario.