[1]王希鹏,李永,李智,等.融合图像深度的抗遮挡目标跟踪算法[J].郑州大学学报(工学版),2021,42(05):19-24.[doi:10.13705/j.issn.1671-6833.2021.05.011]
　Wang Xipeng,Li Yong,Li Zhi,et al.Anti-occlusion Target Tracking Algorithm Based on Image Depth[J].Journal of Zhengzhou University (Engineering Science),2021,42(05):19-24.[doi:10.13705/j.issn.1671-6833.2021.05.011]
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融合图像深度的抗遮挡目标跟踪算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42卷
期数:
2021年05期
页码:
19-24
栏目:
出版日期:
2021-09-10

文章信息/Info

Title:
Anti-occlusion Target Tracking Algorithm Based on Image Depth
作者:
王希鹏; 李永; 李智; 张妍
武警工程大学信息工程学院;
Author(s):
Wang Xipeng; Li Yong; Li Zhi; Zhang Yan;
School of Information Engineering at the University of Armed Police Engineering;
关键词:
Keywords:
siamese network;  deep learning;  target tracking;  monocular depth estimation;  anti-occlusion
DOI:
10.13705/j.issn.1671-6833.2021.05.011
文献标志码:
A
摘要:
由于视频信息的局限性，在遮挡情况下的目标跟踪依然是一个很难解决的问题。针对目标跟踪过程中的遮挡问题，提出将图像深度引入单目标跟踪。首先应用单目图像深度估计算法对图像进行深度估计，获取图像的深度信息；其次，将基于孪生网络的目标跟踪算法与图像深度相结合，构建遮挡判别模块，利用目标深度信息的变化判断遮挡情况；最后，对目标跟踪器的候选框重新排序避免被遮挡物干扰。实验结果表明，该算法能有效地应对遮挡情况对跟踪性能的影响，在跟踪成功率和精确度上均高于其他对比算法。
Abstract:
Due to the limitation of video information, target tracking in the case of occlusion is still a difficult problem to solve. Aiming at the problem of occlusion in the target tracking process, it is proposed to introduce image depth into single target tracking algorithm. Firstly, the monocular image depth estimation algorithm is used to estimate the depth of the image to obtain the depth information of the image. Secondly, the target tracking algorithm based on the siamese region proposal network is combined with the image depth to construct an occlusion discriminating module, which uses the change of the target depth information to determine the occlusion. Finally, the occlusion discrimination score and the anchor response score are weighted integrated. According to the final response score, the anchor of the target tracker is reordered to avoid interference by obstructions. Experimental results on the OTB-2015 dataset show that the algorithm can effectively deal with the influence of occlusion on tracking performance, with an average success rate of 0.623 and an average tracking accuracy of 0.853, which is 1.7% and 0.9% higher than the benchmark algorithm, repectively.

参考文献/References:

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更新日期/Last Update: 2021-10-11