CAJ | 학술논문

利用粒子空间分布和粒子观测概率信息定义了一种目标特征的确定性度量方法，将该度量应用到传统的多特征融合跟踪算法中，实现了目标特征加权值的自适应调整，使得不同场景及外部干扰条件下各种目标特征信息对跟踪结果的贡献达到最优。试验表明，基于确定性度量的跟踪算法较传统的单一特征跟踪及固定加权值的多特征融合跟踪算法有着更好的鲁棒性。
이용입자공간분포화입자관측개솔신식정의료일충목표특정적학정성도량방법，장해도량응용도전통적다특정융합근종산법중，실현료목표특정가권치적자괄응조정，사득불동장경급외부간우조건하각충목표특정신식대근종결과적공헌체도최우。시험표명，기우학정성도량적근종산법교전통적단일특정근종급고정가권치적다특정융합근종산법유착경호적로봉성。
The certainty measurement of object feature is defined by using spatial distribution and observation proba-bility of particle.The measurement is introduced into the object tracking algorithm based on multi-feature fusion,and the weighted value of object feature is adjusted adaptively.So the object feature can adaptively adjust its contribution to tracking in different scenes and external disturbance.The results of the experiment show that the algorithm based on certainty measurement is more robust than traditional algorithm using single feature or multi-feature fusion with fixed weighted value.