[1]姜文涛,张大鹏.优化分类的弱目标孪生网络跟踪研究[J].智能系统学报,2023,18(5):984-993.[doi:10.11992/tis.202211043]
JIANG Wentao,ZHANG Dapeng.Research on weak object tracking based on Siamese network with optimized classification[J].CAAI Transactions on Intelligent Systems,2023,18(5):984-993.[doi:10.11992/tis.202211043]
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JIANG Wentao,ZHANG Dapeng.Research on weak object tracking based on Siamese network with optimized classification[J].CAAI Transactions on Intelligent Systems,2023,18(5):984-993.[doi:10.11992/tis.202211043]
优化分类的弱目标孪生网络跟踪研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第5期
页码:
984-993
栏目:
学术论文—机器学习
出版日期:
2023-09-05
- Title:
- Research on weak object tracking based on Siamese network with optimized classification
- Keywords:
- computer vision; object tracking; weak object; deformable convolution; prior spatial score; localization quality score; feature extraction; convolutional neural network; siamese network
- 分类号:
- TP391.4
- 摘要:
- 针对传统孪生网络算法对模糊、低分辨率等弱目标跟踪效果不佳的问题，提出了优化分类预测的孪生网络算法。首先通过引入可变形卷积模块，提高骨干网络特征提取能力，其次在分类分支中引入位置信息，提升算法对于目标的识别能力，最后使用轻量级的卷积神经网络进行分类预测和边界预测任务，在规避多尺度测试的同时，进一步利用了图像的语义信息，使跟踪结果具有较高的可信度。在OTB2015、VOT2018公共数据集上进行的大量实验表明，本文算法综合表现优于主流同类算法，对模糊、形变、快速运动等多种复杂场景具有较好的适应性。
- Abstract:
- To address the problem that traditional Siamese networks are poor in tracking weak objects in blurry and low resolution, this study proposed a Siamese network with optimized classification prediction. First, the feature extraction ability of the backbone network was improved by introducing a deformable convolution module. Second, this algorithm enhances the ability of the backbone network to extract features by introducing the location information in the classification branch. Finally, a lightweight convolutional neural network was used for the prediction of classification and boundary to further utilize the semantic information of the images while avoiding multiscale testing, making the tracking results more reliable. Many experiments have analyzed OTB2015 and VOT2018 datasets, and the results show that the comprehensive performance of this algorithm is better than those of the mainstream similar algorithms, demonstrating excellent adaptability to complex scenes such as motion blur, deformation, and fast motion.
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备注/Memo
收稿日期:2022-11-30。
基金项目:国家自然科学基金项目（61172144）；辽宁省自然科学基金项目（20170540426）；辽宁省教育厅基金项目（LJYL049）.
作者简介:姜文涛，副教授，博士，主要研究方向为图像处理、模式识别、人工智能。参与国家及省级项目2项，发表学术论文20余篇;张大鹏，硕士研究生，主要研究方向为图像处理、模式识别、人工智能
通讯作者:姜文涛.E-mail：lntuwulue@163.com
更新日期/Last Update:
1900-01-01