School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Comput Intell Neurosci. 2022 Aug 9;2022:8051876. doi: 10.1155/2022/8051876. eCollection 2022.
Machine learning only uses single-channel grayscale features to model the target, and the filter solution process is relatively simple. When the target has a large change relative to the initial frame, the tracking effect is poor. When there is the same kind of target interference in the target search area, the tracking results will be poor. The tracking algorithm based on the fully convolutional Siamese network can solve these problems. By learning the similarity measurement function, the similarity between the template and the target search area is evaluated, and the target area is found according to the similarity. It adopts offline pre-training and does not update online for tracking, which has a faster tracking speed. According to this study, (1) considering the accuracy and speed, the target tracking algorithm based on correlation filtering performs well. A sample adaptive update model is introduced to eliminate unreliable samples, which effectively enhances the reliability of training samples. With simultaneous changes in illumination and scale, fast motion and in-plane rotation IPR can still be maintained. (2) Determined by calculating the Hessian matrix, in the Struck function, Bike3 parameter adjustment can achieve fast tracking, and Boat5 ensures that the system stability is maintained in the presence of interference factors. The position of the highest scoring point in the fine similarity score map of the same size as the search image is obtained by bicubic interpolation as the target position. (3) The parallax discontinuity caused by the object boundary cannot be directly processed as a smooth continuous parallax. The MeanShift vector obtained by calculating the target template feature and the feature to be searched can increase the accuracy by 53.1%, reduce the robustness by 31.8%, and reduce the error by 28.6% in the SiamVGG algorithm.
机器学习仅使用单通道灰度特征来对目标建模，并且滤波解决方案过程相对简单。当目标相对于初始帧发生较大变化时，跟踪效果较差。当目标搜索区域中存在相同类型的目标干扰时，跟踪结果将较差。基于全卷积 Siamese 网络的跟踪算法可以解决这些问题。通过学习相似度测量函数，评估模板与目标搜索区域之间的相似度，并根据相似度找到目标区域。它采用离线预训练，不进行在线更新进行跟踪，因此跟踪速度更快。根据这项研究，（1）考虑到准确性和速度，基于相关滤波的目标跟踪算法表现良好。引入了样本自适应更新模型以消除不可靠的样本，这有效地增强了训练样本的可靠性。即使在光照和尺度同时变化、快速运动和平面内旋转 IPR 的情况下，也能保持快速跟踪。（2）由 Hessian 矩阵决定，在 Struck 函数中，Bike3 参数调整可以实现快速跟踪，而 Boat5 确保在存在干扰因素的情况下系统稳定性得以维持。通过双三次插值获得与搜索图像大小相同的精细相似度得分图中得分最高的点的位置作为目标位置。（3）由于物体边界引起的视差不连续性，不能直接作为平滑连续视差进行处理。通过计算目标模板特征和待搜索特征得到的 MeanShift 向量，可以将 SiamVGG 算法的准确性提高 53.1%，鲁棒性降低 31.8%，误差降低 28.6%。
