基于深度去噪自编码器的RGB-D视频目标跟踪

doi:10.16182/j.issn1004731x.joss.201811028

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (11): 4276-4283.doi: 10.16182/j.issn1004731x.joss.201811028

基于深度去噪自编码器的RGB-D视频目标跟踪

姜明新¹, 潘志庚^2,3, 王兰芳⁴, 胡铸鑫^{5, *}

1. 淮阴工学院电子信息工程学院,江苏淮安223003;
2. 玖的虚拟现实研究院,广州玖的数码科技有限公司,广州广东510623;
3. 杭州师范大学数字媒体与人体交互研究中心,浙江杭州 310027;
4. 淮阴工学院计算机与软件工程学院,江苏淮安 223003;
5. 温州大学教师教育学院,浙江温州325035

收稿日期:2018-04-17 修回日期:2018-05-31 发布日期:2019-01-04
作者简介:姜明新(1979-), 女, 黑龙江双城, 博士后,副教授, 硕导, 研究方向为目标跟踪和计算机视觉。
基金资助:
国家重点科技支撑项目(2017YFB1002803),国家自然科学基金(61332017)

Visual Object Tracking Algorithm Based on Deep Denoising Autoencoder over RGB-D Data

Jiang Mingxin¹, Pan Zhigeng^2,3, WangLanfang⁴, Hu Taoxin^{5, *}

1. Faculty of Electronic information Engineering, Huaiyin Institute of Technology, Huaian 223003, China;
2. Nine D virtual reality research institute, Guangzhou Nine D digital technology co., Ltd. Guangzhou 510623, China;
3. Digital Media &Interaction Research Center, Hangzhou Normal University, Hangzhou 310012, China;
4. Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China;
5. College of Techer Education, Wenzhou University, Wenzhou 325035, China

Received:2018-04-17 Revised:2018-05-31 Published:2019-01-04

摘要/Abstract

摘要： 提出了一种基于跨模式特征深度学习的RGB-D视频目标跟踪算法。构建跨模式稀疏去噪自编码器深度学习网络,提取RGB-D视频数据中样本的跨模式特征。将样本的跨模式特征输入到逻辑回归分类器中,获得置信分数,利用逻辑回归分类器的输出来构建观测似然模型。通过粒子滤波算法来实现RGB-D视频数据中的目标跟踪。实验结果表明,提出的视频目标跟踪算法对遮挡、旋转、光照变化等具有较强的鲁棒性,能够稳定的跟踪目标,具有较高的成功率。

关键词: 跨模式特征, 稀疏去噪自编码器, 深度学习, RGB-D视频目标跟踪

Abstract: A visual object tracking algorithm based on cross-modality features deep learning over RGB-D data is proposed. A sparse denoising autoencoder deep learning network is constructed, which can extract cross-modal features of the samples in RGB-D video data. The cross-modal features of the samples are input to the logistic regression classifier, the observation likelihood model is established according to the confidence score of the classifier, and the reasonable state transition model is established. The object tracking results over RGB-D data are obtained using particle filtering algorithm. Experimental results show that the proposed method has strong robustness to abnormal changes. The algorithm can steadily track multiple targets with higher accuracy.

Key words: Cross-modal features, sparse denoising autoencoder, deep learning, visual object tracking over RGB-D data

中图分类号:

TP391

姜明新, 潘志庚, 王兰芳, 胡铸鑫. 基于深度去噪自编码器的RGB-D视频目标跟踪[J]. 系统仿真学报, 2018, 30(11): 4276-4283.

Jiang Mingxin, Pan Zhigeng, WangLanfang, Hu Taoxin. Visual Object Tracking Algorithm Based on Deep Denoising Autoencoder over RGB-D Data[J]. Journal of System Simulation, 2018, 30(11): 4276-4283.

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基于深度去噪自编码器的RGB-D视频目标跟踪

Visual Object Tracking Algorithm Based on Deep Denoising Autoencoder over RGB-D Data

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