一种基于SiameseRPN 的模糊视频目标跟踪算法

为提高追踪准确率， 提出一种针对模糊视频的轻量级视频跟踪算法。采用主流对抗生成网络模型 DeblurGAN V2 进行去模糊处理，交由基于改进SiameseRPN 目标跟踪算法进行目标跟踪。为实现轻量化，将目标跟 踪的特征网络替换为EfficientNet 网络，改进注意力机制为ECANet 以捕捉多通道信息，并在GoPro 数据集上进行测 试。测试结果表明：相比SiameseRPN 算法，该算法能实现更高的追踪准确率，帧率能达到实时性要求，具有一定 的借鉴意义。

A Blur Video Target Tracking Algorithm Based on SiameseRPN

In order to improve the tracking accuracy, a lightweight video tracking algorithm for fuzzy video is proposed. The mainstream countermeasure generation network model DeblurGAN V2 is used for deblurring, and the improved SiameseRPN target tracking algorithm is used for target tracking. In order to achieve lightweight, the feature network of target tracking is replaced by EfficientNet network, and the attention mechanism is improved to ECANet to capture multi-channel information, which is tested on GoPro data set. The test results show that compared with the Siamese RPN algorithm, the proposed algorithm can achieve higher tracking accuracy, and the frame rate can meet the real-time requirements, which has certain reference significance.