基于孪生神经网络的两阶段目标跟踪方法

深度学习技术使目标跟踪的精度和鲁棒性得到了很大提高，基于孪生网络的跟踪方法通过在大规模数据集上进行训练，使模型能应对目标的各种形变，缺点是无法排除相似目标的干扰。为此，提出了一种基于孪生网络的两阶段目标跟踪方法。首先，采用修改后的残差网络提取性能更优的深度特征。区域建议网络通过相关滤波调制自适应更新模板，结合时域信息过滤掉易区分的负样本；然后，通过感兴趣池化层提取候选区域固定尺度的特征，并馈送到验证网络进行更精细的分类与回归。为了提升网络对高难度样本的区分能力，采用正负样本对联合训练的方式提高特征匹配的性能。在OTB100、VOT标准测试集和UAV123无人机航拍数据集上进行了评测，实验结果表明:所提方法能明显改进基准算法的性能。

Two-stage object tracking method based on Siamese neural network

Through the introduction of deep learning, the accuracy and robustness of object tracking have been greatly improved. Siamese network based trackers can deal with various deformation of target through training on large-scale datasets, but that makes it difficult to eliminate the interference of similar targets. Therefore, a two-stage tracking method based on Siamese network was proposed. Firstly, the modified residual network was used to extract the deep feature with better performance. Through integrating the temporal information, the template of the region proposal network was adaptively updated through correlation filter modulation, so as to filter out the easily distinguished negative samples. Then, the fixed scale features of candidate regions were extracted by the region-of-interest pooling and fed to the verification network for more refined classification and regression. In order to improve the network's ability to discriminate difficultly distinguished samples, joined training method combining the positive and negative samples was adopted to improve the performance of feature matching. The performance of the proposed method was evaluated on the OTB100, VOT standard benchmarks and the UAV123 aerial benchmark. The experimental results demonstrate that the proposed method can significantly improve the performance of the baseline.