融合位置信息注意力的孪生弱目标跟踪算法

doi:10.3778/j.issn.1002-8331.2112-0076

摘要/Abstract

摘要： 经典孪生网络弱特征目标跟踪存在鲁棒性差的问题。为此，设计了一种融合目标二维位置信息注意力机制的孪生网络算法。该算法以区域候选孪生网络（siamese region proposal network，SiamRPN）为基础，包括特征提取网络部分和相似度计量部分。在特征提取网络部分，引入了位置信息注意力模块来提取目标特征二维位置信息以提升网络对弱目标的特征提取能力。采用了轻量深度特征提取网络MobileNetV2来减少特征提取网络部分模型参数和计算量；在相似度计量部分，基于多层特征融合的相似度计量方法深入挖掘特征提取网络浅层特征的定位信息和深层特征的语义信息，加强了算法的跟踪准确性和定位精度。实验结果表明，所提出的算法在UAV123数据集上成功率相较于SiamRPN基础算法提升了12.6%，跟踪精度提升了8.4%，且跟踪速度每秒74帧，在提升成功率的同时满足了实时性的要求。

关键词: 目标跟踪, 深度学习, 孪生网络, 注意力模型

Abstract: The weak feature target tracking of the classic siamese network has the problem of poor robustness. For this reason, a siamese network algorithm that integrates the attention mechanism of the target’s two-dimensional position information is designed. This algorithm is based on the siamese region proposal network（SiamRPN） target tracking algorithm, introduces the location information attention module in the feature extraction network part to extract the two-dimensional location information of the target feature to calculate the weight of the feature channel, and improves the feature extraction ability of the network for weak targets. In the feature extraction backbone network part, lightweight deep feature extraction backbone network MobileNetV2 is used instead of AlexNet, which reduces the model parameters and the amount of calculation while improving the feature extraction capabilities of the backbone network. In the similarity measurement module, the similarity measurement method of multi-layer feature fusion is adopted to make full use of the position information of the shallow features of the deep network and the semantic information of the deep features to strengthen the tracking accuracy and positioning accuracy of the algorithm. Experimental results show that compared with the basic algorithm, the success rate of this algorithm is increased by 12.6%, and the precision is increased by 8.4%. The tracking speed reaches 74?frames per second, which meets the real-time requirements.

Key words: object tracking, deep learning, siamese network, attention model

韦健, 赵旭, 李连鹏. 融合位置信息注意力的孪生弱目标跟踪算法[J]. 计算机工程与应用, 2023, 59(7): 198-206.

WEI Jian, ZHAO Xu, LI Lianpeng. Siamese Network Weak Target Tracking Algorithm Fused with Location Information Attention[J]. Computer Engineering and Applications, 2023, 59(7): 198-206.

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