基于深度学习的交互似然目标跟踪算法

doi:10.11896/j.issn.1002-137X.2019.02.043

摘要/Abstract

摘要： 针对传统的视频跟踪算法对视频跟踪的精度不足以及主成分分析(PCA)的非线性拟合能力较弱的问题,将卷积神经网络与交互似然(IL)算法相结合,在深度学习的基础上对粒子滤波算法进行了优化改进。将核主成分分析(KPCA)网络应用于视频跟踪来获取目标的深层次特征表达,并采用一种新的交互似然图像跟踪器,非迭代地计算,对不同区域进行跟踪取样来减少数据之间的关联需求。在图像集上将所提算法与多种改进算法进行评估对比,结果表明所提算法具有非常好的鲁棒性及精确性。

关键词: 核主成分分析, 交互似然, 卷积神经网络, 目标跟踪, 深度学习

Abstract: The traditional video target tracking methods usually prossess low accuracy.This paper proposed an improved scheme based on convolution neural network and the interactive likelihood algorithm,and optimized the particle filter algorithm on the basis of deep learning.To address the issue of deficient nonlinear fitting ability of the principal component analysis (PCA),a kernel principal component analysis (KPCA) tracking algorithm was provided to obtain the deeper characteristic expression of the target.Then,a novel interactive likelihood (ILH) method was performed for image-based trackers,which can non-iteratively compute the sampling of areas belonging to different targets and thus reducing the requirement for data associations.The performance of the presented algorithm was evaluated in comparison with several related algorithms on image datasets.The experimental results demonstrate the great robustness and accuracy of the proposed algorithm.

Key words: Convolutional neural network(CNN), Deep learning, Interactive likelihood(IL), Kernel principal component analysis(KPCA), Target tracking

中图分类号:

TP181

张明月, 王静. 基于深度学习的交互似然目标跟踪算法[J]. 计算机科学, 2019, 46(2): 279-285. https://doi.org/10.11896/j.issn.1002-137X.2019.02.043

ZHANG Ming-yue, WANG Jing. Interactive Likelihood Target Tracking Algorithm Based on Deep Learning[J]. Computer Science, 2019, 46(2): 279-285. https://doi.org/10.11896/j.issn.1002-137X.2019.02.043

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