基于条件对抗生成孪生网络的目标跟踪
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(沈阳理工大学自动化与电气工程学院,沈阳110159)

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E-mail: 20080571@qq.com.

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TP273

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国家重点研发计划项目(2017YFC0821001);辽宁省科技厅自然基金指导计划项目(2019-ZD-0252); 辽宁省教育厅高等学校基本科研项目(LG201709).


Conditional generative adversarial siamese networks for object tracking
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(School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang110159,China)

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    摘要:

    为解决被跟踪目标在快速剧烈运动时,因运动模糊和低分辨率使模型发生漂移,导致跟踪器跟踪效果 变差甚至跟踪失败的问题,对全卷积孪生网络跟踪算法(SiamFC)进行改进,提出一种基于条件对抗生成 孪生网络的目标跟踪算法(CGANSiamFC).首先,在SiamFC框架的基础上嵌入条件对抗生成网络模块,对输入 的低分辨率模糊视频帧去模糊化;然后,利用全卷积孪生网络对重建后的视频帧进行特征提取,使模型的表征能力 进一步提升;最后,采用分离训练和线上组合方式对改进后的跟踪算法进行训练和测试,并使用视觉跟踪基准 数据集OTB100对改进后的跟踪算法进行性能评估;同时,为了充分验证所提出算法的有效性,使用传统的Lucy-Richardson去模糊算法对SiamFC加以改进,并与CGANSiamFC进行对比分析.实验结果表明,所提出的CGANSiamFC综合精确率和成功率指标比原SiamFC分别提高9%和8%,对运动模糊和低分辨率运动目标具有良好的跟踪效果.

    Abstract:

    In order to solve the problem that the model drifts due to the motion blur and low resolution when the tracked target moves fast and violently, which leads to the poor tracking effect of the tracker or even the tracking failure, this paper improves the fully convolutional siamese networks for object tracking (SiamFC), and proposes a target tracking algorithm based on conditional generative adversarial siamese networks for object tracking (CGANSiamFC). Firstly, the conditional generative adversarial network module is embedded on the basis of the SiamFC framework to deblur the input low-resolution blurred video frames. Then, the fully convolutional siamese networks perform feature extraction on the reconstructed video frame, which improve the model's characterization ability. Finally, this paper uses separate training and online combination methods to train and test the improved tracking algorithm, and uses the visual tracking benchmark data set OTB100 to evaluate the performance of the improved tracking algorithm. At the same time, in order to fully verify the effectiveness of the proposed algorithm, this paper uses the traditional Lucy-Richardson deblurring algorithm to improve the SiamFC, which is compared with the CGANSiamFC. The experimental results show that the comprehensive accuracy and success rate indicators of the CGANSiamFC proposed are 9% and 8% higher than that of the original SiamFC, respectively, and it has good tracking effects on motion blur and low-resolution moving targets.

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宋建辉,张甲,刘砚菊,等.基于条件对抗生成孪生网络的目标跟踪[J].控制与决策,2021,36(5):1110-1118

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  • 在线发布日期: 2021-04-08
  • 出版日期: 2021-05-20