系统工程与电子技术 ›› 2019, Vol. 41 ›› Issue (9): 1945-1954.doi: 10.3969/j.issn.1001-506X.2019.09.05

• 电子技术 • 上一篇    下一篇

空间注意机制下的自适应目标跟踪

谢瑜, 陈莹   

  1. 江南大学轻工过程先进控制教育部重点实验室, 江苏 无锡 
  • 出版日期:2019-08-27 发布日期:2019-08-20

Adaptive object tracking based on spatial attention mechanism

XIE Yu, CHEN Ying   

  1. Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi 214122, China
  • Online:2019-08-27 Published:2019-08-20

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摘要：

针对现有的分层卷积特征跟踪算法在遭遇多种复杂环境时会发生跟踪失败的问题,提出一种空间注意机制下的自适应目标跟踪算法。在跟踪的过程中,根据当前帧的颜色直方图基于贝叶斯分类器建立空间注意机制,在提取VGGNet19中多层卷积特征后分别与空间注意图进行融合,从而构建稳健的目标表观模型。之后利用学习到的相关滤波器得到各响应值,通过加权求和准则求出最终响应,同时利用帧差法调整学习速率,最终实现自适应的目标跟踪。实验结果表明,所提算法在大多数复杂环境下的跟踪准确度和鲁棒性均优于现有的跟踪算法。

关键词: 目标跟踪, 相关滤波, 卷积特征, 空间注意

Abstract:

Aiming at the failure of existing hierarchical convolutional features for the visual tracking algorithm in complex environments, an adaptive object tracking algorithm based on the spatial attention mechanism is proposed. According to the color histogram of the current frame, the spatial attention mechanism is established based on the Bayesian classifier. After extracting multi-layer convolutional features in VGGNet19, the spatial attention map is fused with convolutional features respectively to construct more robust target apparent models. The response is obtained by using the correlation filter, and the final response is achieved by the weighted summation criterion. The adaptive update of the filter template is implemented by using the frame difference method to adjust the learning rate in the tracking process. The experimental results show that the tracking accuracy and robustness of the proposed algorithm are better than the existing state-of-the-art tracking algorithms in most complex environments.

Key words: object tracking, correlation filter, convolutional features, spatial attention

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