School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2018 Dec 25;19(1):73. doi: 10.3390/s19010073.
Aiming at the problem of poor robustness and the low effectiveness of target tracking in complex scenes by using single color features, an object-tracking algorithm based on dual color feature fusion via dimension reduction is proposed, according to the Correlation Filter (CF)-based tracking framework. First, Color Name (CN) feature and Color Histogram (CH) feature extraction are respectively performed on the input image, and then the template and the candidate region are correlated by the CF-based methods, and the CH response and CN response of the target region are obtained, respectively. A self-adaptive feature fusion strategy is proposed to linearly fuse the CH response and the CN response to obtain a dual color feature response with global color distribution information and main color information. Finally, the position of the target is estimated, based on the fused response map, with the maximum of the fused response map corresponding to the estimated target position. The proposed method is based on fusion in the framework of the Staple algorithm, and dimension reduction by Principal Component Analysis (PCA) on the scale; the complexity of the algorithm is reduced, and the tracking performance is further improved. Experimental results on quantitative and qualitative evaluations on challenging benchmark sequences show that the proposed algorithm has better tracking accuracy and robustness than other state-of-the-art tracking algorithms in complex scenarios.
针对基于单一颜色特征的目标跟踪算法在复杂场景中鲁棒性差、跟踪效果不佳的问题，根据相关滤波（CF）跟踪框架，提出了一种基于降维双通道颜色特征融合的目标跟踪算法。首先，对输入图像分别进行颜色名称（CN）特征和颜色直方图（CH）特征提取，然后通过 CF 方法对模板和候选区域进行相关运算，分别得到目标区域的 CH 响应和 CN 响应。提出了一种自适应特征融合策略，对 CH 响应和 CN 响应进行线性融合，得到具有全局颜色分布信息和主色信息的双通道特征响应。最后，根据融合后的响应图，在融合后的响应图的最大值处估计目标的位置，该最大值对应的位置即为目标的估计位置。该方法是在 Staple 算法的框架内进行融合，并在尺度上进行主成分分析（PCA）降维，降低了算法的复杂度，进一步提高了跟踪性能。在具有挑战性的基准序列上进行的定量和定性评估实验结果表明，该算法在复杂场景下的跟踪精度和鲁棒性均优于其他先进的跟踪算法。
