基于目标和背景子空间组合的目标跟踪算法

Visual Tracking Algorithm Based on the Combination of Target and Background Subspace

针对基于单一特征的视觉跟踪方法无法对视频目标进行全面的描述且在复杂环境下此类跟踪方法的鲁棒性较低的问题。文中在粒子滤波跟踪的框架下,利用结构稀疏表示方法和视频帧的灰度特征和HOG特征,提出了一种基于目标子空间和背景子空间相结合的视频目标跟踪算法。提出的跟踪算法基于目标子空间和背景子空间对字典模板进行构造和更新,且综合考虑了目标的全局和局部信息,有效提升了视觉跟踪的鲁棒性能。

Single feature-based Visual tracking cannot describe the video object thoroughly,and the robustness of such tracking method is low under complex conditions.According to this problem,this paper proposes a visual tracking algorithm based on the combination of target and background subspace by using structure sparse representation method and the gray and HOG(Histogram of Oriented Gradient)feature in the framework of particle filter.The proposed tracking algorithm constructs and updates the dictionary template based on the target subspace and the background subspace,and considers the global and local information of the target,which effectively improves the robust performance of the visual tracking.