基于动态特征融合的粒子滤波目标跟踪算法

提出一种基于动态特征融合的粒子滤波目标跟踪算法。选择具有互补性的灰度直方图和梯度直方图特征共同描述目标模型，然后在目标跟踪过程中，根据特征对目标和背景的区分程度动态地调整每个特征的置信度，对目标模型进行在线动态建模和更新，从而提高目标模型描述的准确度，并进一步提高粒子滤波算法的跟踪精度。实验结果表明:在对典型场景下的目标跟踪过程中，提出的算法比单独使用一种特征的粒子滤波算法具有更高的跟踪精度和更稳定可靠的跟踪性能。

Particle filter object tracking algorithm based on dynamic feature fusion

A particle filter object tracking algorithm based on dynamic feature fusion is proposed. The presented algorithm uses the complementary features, which are gray histogram and gradient histogram, to represent the object model. In the tracking procession, the confidence for each feature is adjusted according to the discrimination between the object and the background, and the object model is established and updated onlinely. The presented method can improve the accuracy of the object modeling and furthermore improve the accuracy of the particle filter tracking algorithm. Experimental results show that, in the representative object tracking scenes, the proposed algorithm can gain more accurate and more reliable tracking performance.