融合稀疏重构图像显著性的相关滤波跟踪

Correlation Filter Tracking Algorithm Fusing Image Saliency via Sparse Reconstruction

针对相关滤波跟踪中目标在剧烈形变时会发生滤波模板漂移,以及在复杂场景中目标跟踪鲁棒性较差的问题,提出一种融合稀疏重构图像显著性的相关滤波跟踪算法。在跟踪过程中,通过超像素分割提取背景模板来稀疏重构目标颜色相关,构建目标颜色模型得到跟踪检测分数,将该检测分数与相关滤波检测分数进行融合,根据融合响应,利用峰值旁瓣比调整模板更新速度来解决遮挡下的更新策略问题,同时利用中心先验图对存在误差的稀疏重构图进行修正,使得该目标跟踪框架能适应形变、光照等复杂变化。实验表明,该算法在准确性和鲁棒性方面要优于其他算法。

To address the problems of target deformation and poor robustness of tracking in complex environment, a correlation filter tracking algorithm fusing image saliency via sparse reconstruction is proposed. In the process of target tracking,background template is extracted by superpixel segmentation. Target color correlation is obtained based on sparse reconstruction. Then the correlation filter detection score is combined with the target color detection score for accurate tracking.Template update speed is adjusted by the peak sidelobe ratio which is based on the fused detection score. Meanwhile, a center prior is established to correct the sparse reconstruction based saliency map. The proposed target tracking framework can adapt to deformation, illumination and other complexities. Experiments show that this algorithm is superior to other state-of-art tracking algorithms in terms of accuracy and robustness.