多空间分辨率自适应特征融合的相关滤波目标跟踪算法

相关滤波算法因无法充分利用深度特征和浅层特征的互补特性而限制跟踪性能.针对该问题,文中提出多空间分辨率自适应特征融合的相关滤波目标跟踪算法.首先,使用更深的ResNet-50网络提取深度特征,提高特征表示在跟踪过程中的鲁棒性和鉴别性.再针对不同特征具有不同空间分辨率的特点,从视频帧中分割不同尺度的图像块作为搜索区域,更好地平衡边界效应和样本数目.最后,引入自适应特征融合方法,以自适应的权重融合两类特征的响应图,充分利用其互补特性.在多个标准数据集上的实验证实文中算法的有效性和鲁棒性.

Object Tracking with Multi-spatial Resolutions and Adaptive Feature Fusion Based on Correlation Filters

Correlation filter(CF)based trackers cannot take advantage of the complementary characteristic of deep features and shallow features.To mitigate this problem,an object tracking algorithm with multi-spatial resolutions and adaptive feature fusion based on correlation filter is proposed.Firstly,ResNet-50 is employed to extract deep features and enhance the discrimination and robustness of feature representation during tracking.Additionally,according to the characteristic of different features with different spatial resolutions,image patches in different scales are segmented from video frame as the search area to balance the boundary effect and the number of samples.Finally,an adaptive feature fusion strategy is introduced to fuse the response maps corresponding to two kinds of features with adaptive weights to utilize the complementary characteristic.The experiments on multiple standard datasets verify the effectiveness and robustness of the proposed algorithm.