融合坐标信息与模板更新的孪生网络目标跟踪

应对孪生网络单目标跟踪算法在跟踪中遇到背景杂乱、相似物影响、遮挡等复杂场景的问题导致跟踪系统精度和成功率下降的问题,提出一种融合坐标注意力机制和模板更新的跟踪算法MCUSiamRPN (MobileNet coordinate attention and updating of template SiamRPN).在SiamRPN算法基础上,采用改进的MobileNetV3为特征提取网络,多层特征信息分别送入坐标注意力模块,进行特征融合,丰富语义信息;设计了一种自适应模板更新模块,结合初始模板和当前帧的模板用于估计下一帧的最佳模板更新模板信息.在OTB100和UAV123两个数据集上进行测试,结果显示:相比于基准算法Siam RPN,精度分别提升了5.3%和3.7%;成功率分别提升了3.7%和5.2%,验证了该算法的有效性.

Siamese Network Target Tracking Fused with Coordinate Information and Template Update

The single target tracking algorithm for siamese networks would encounter complex scenes such as background clutter, the influence of similar objects, and occlusion, which leads to a decrease in the accuracy and success rate of the tracking system. In response, this study proposes a tracking algorithm combining the coordinate attention mechanism and template update, i.e., MobileNet coordinate attention and updating of template SiamRPN (MCUSiamRPN). On the basis of the SiamRPN algorithm, the improved MobileNetV3 is used as the feature extraction network, and the multi-layer feature information is sent to the coordinate attention module to fuse features and enrich semantic information. An adaptive template updating module is designed, which combines the initial template and the template of the current frame to estimate the best template of the next frame for template information updating. The test results on OTB100 and UAV123 data sets show that compared with the benchmark algorithm SiamRPN, the proposed one has precision improved by 5.3% and 3.7% and achieves a success rate increased by 3.7% and 5.2%, respectively, which verifies the effectiveness of the developed algorithm.