针对传统手工特征表达能力不足和滤波器模型存在误差累积的影响,提出一种融合多层卷积特征自适应更新的目标跟踪算法。该算法采用分层卷积神经网络提取图像特征,利用线性加权的方法融合多层卷积特征预测目标位置;利用多尺度下目标卷积特征确定目标最佳尺度;利用平均峰值相关能量评价目标响应的置信度,根据相邻两帧目标图像的帧差均值和位移评估目标的运动情况,根据预测位置可信度和目标图像外观变化,调整滤波器模型的学习率。在OTB-2013公开测试集上验证本算法性能,并与现有基于相关滤波的主流运动目标跟踪算法进行相比,实验结果表明,本算法在精度和成功率上表现更优,且在复杂情况下稳健性更强。

Herein, we propose a target-tacking algorithm based on adaptive updating of multilayer convolutional features to address the insufficiency of traditional manual feature expression and the error accumulation of filter models. First, the algorithm uses a layered convolutional neural network to extract the image features, and fuses multi-convolution features through linear weighting to predict the target position. Then, the multiscale target convolution features are used to determine the target optimal scale. Finally, the average peak correlation energy is used to evaluate the confidence of the target response. We evaluate the motion condition of the target according to the frame differential mean and displacement of the two adjacent frames of the target image, and adjust the learning rate of the filter model according to the predicted position credibility and the appearance of the target image. The performance of the algorithm is verified using the OTB-2013 public test set and compared with the existing mainstream moving target tracking algorithm based on correlation filtering. Experimental results show that the proposed algorithm provides higher accuracy and success rate, and is more robust in complex cases.