基于可变形卷积的孪生网络目标跟踪算法
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作者单位:

江南大学物联网学院

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中图分类号:

TP273

基金项目:

国家自然科学基金项目(61573167)、教育部科技发展中心“云数融合科教创新”基金(2017A13055)


Target Tracking Based on Deformable Convolution Siamese Network
Author:
Affiliation:

School of Internet of Things, Jiangnan University

Fund Project:

The National Natural Science Foundation of China under Grant NO. 61573167、Fund for “Integration of Cloud Computering and Big Data” of Innovation of Science and Education under Grant NO. 2017A13055

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    摘要:

    针对基于孪生网络的大多数目标跟踪算法中骨干网络特征提取能力弱、模板无法适应目标变化等问题。在SiamFC算法的基础上提出了基于可变形卷积的孪生网络算法(DCSiam)。首先,采用可变形卷积模块在不同方向上学习多层特征数据的自适应偏移量,增大卷积过程中的有效感受野,通过多层可变形互相关融合得到最终响应图,以增强骨干网络的深层语义特征提取能力;最后,采用一种高置信度的模板在线更新策略。每隔固定帧计算响应图的峰值旁瓣比与最大值作为更新依据,使用加权的方式融合特征以更新模板。使用OTB2013,OTB2015,VOT2016与VOT2017这4个公共基准数据集对所提算法进行跟踪性能评估。实验结果表明,在OTB2015数据集上,DCSiam算法整体精确率、成功率较基线分别提高了9.5%与7.5%,很好的实现了复杂情况下的目标跟踪,验证了所提算法的有效性。

    Abstract:

    In most target tracking algorithms based on siamese network, the feature extraction ability of the backbone network is weak and the template can’t adapt to the change of target. A deformable convolution siamese network algorithm(DCSiam) is proposed based on SiamFC algorithm. Firstly, to enhance the capability of deep semantic feature extraction of backbone network, the deformable convolution module is used to learn the adaptive offset of multi-layer feature data in different directions, and the effective receptive field in the convolution process is increased. The final response map is obtained by multi-layer deformable cross-correlation fusion. Finally, an online template updating strategy with high confidence is adopted. The peak sidelobe ratio an the maximum value of the response graph were calculated every fixed frame as the basis for updating, and the features were fused were in a weighted way to update the template. The tracking performance of the proposed algorithm is evaluated using four common benchmark datasets:OTB2013、OTB2015、VOT2016 and VOT2017. The experimental results show that the overall accuracy and success rate of the DCSiam algorithm are increased by 9.5% and 7.5% respectively compared with the baseline on the OTB2015 data set, which well realizes the target tracking in complex situations and verifies and verifies the effectiveness of the proposed algorithm.

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  • 收稿日期:2021-01-16
  • 最后修改日期:2021-04-27
  • 录用日期:2021-05-12
  • 在线发布日期: 2021-07-01
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