基于融合特征的多尺度快速相关滤波跟踪算法

针对复杂场景下目标遮挡和尺度变化所导致的跟踪效果不佳问题,提出一种基于融合特征的多尺度快速相关滤波跟踪算法。首先,对目标的3种特征降维融合构成特征矩阵;其次,采用主成分分析思想实时地提取显著特征,重构特征矩阵,在有效降维的同时训练位置相关滤波器;最后,利用融合特征矩阵训练尺度相关滤波器,从而准确预测目标位置和尺度。实验部分将改进算法与目前流行的相关滤波跟踪算法进行比较,结果表明,改进算法在目标遮挡和尺度变化场景下跟踪精度较高,平均跟踪速度达到52.5 frame/s。

A multi-scale fast correlation filter tracking algorithm based on fusion features

We propose a multi scale fast correlation filter tracking algorithm based on fusion features to solve the problem of poor tracking effect caused by target occlusion and scale change in complex scenes. Firstly, the dimensions of the three features of the target are reduced and fused to form a feature matrix. Secondly, the principal component analysis is used to extract the salient features in real time, reconstruct the feature matrix, and position correlation filters are trained while reducing the dimension effectively. Finally, the fusion feature matrix is adopted to train scale correlation filters, thus the position and scale of the target is accurately predicted. We compare the improved algorithm with popular correlation filter tracking algorithms by experiment, and the results show that the improved algorithm has a higher tracking accuarcy and an average tracking speed of 52.5 frame/s in scenarios of target occlusion and scale change.