基于视觉显著性特征的自适应目标跟踪

为解决运动目标跟踪过程中候选目标信息描述单一的问题, 提出一种基于视觉显著性特征融合的自适应目标跟踪算法。提取目标颜色、颜色的变化、强度和运动信息构建目标四元数模型, 采用相位谱重建算法检测目标的显著图(Saliency Map), 并根据特征相似度大小自适应调整权值, 融合视觉显著性特征和颜色特征实现目标跟踪。实验结果表明, 该算法能有效克服部分遮挡和背景融合干扰, 从而实现复杂背景下目标的准确跟踪。

Adaptive Object Tracking Based on Visual Significant Feature

In order to solve the singular describing of candidate target in object tracking, an algorithm based on adaptive feature fusion of visual significant feature is proposed. The quaternion model of target composed of intensity, motion, color and the change of color feature is built, and the PQFT (Phase spectrum of Quaternion Fourier Transform) is used to extract saliency map. According to similarity coefficient, the fusion weights are adaptively adjusted and the color feature is combined to ensure accurate tracking. The experimental results demonstrate that the approach can effectively overcome the part occlusion and the interference of background, realizing the accurate tracking under the case of complex background.