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
基于深度学习的交互似然目标跟踪算法
Interactive Likelihood Target Tracking Algorithm Based on Deep Learning
投稿时间:2017-12-22  修订日期:2018-03-13
DOI:
中文关键词:  目标跟踪,深度学习,卷积神经网络,核主成分分析,交互似然
英文关键词:Target tracking,Deep learning,Convolutional neural network(CNN),Kernel principal component analysis(KPCA),Interactive likelihood(IL)
基金项目:
作者单位
张明月 南京工业大学计算机科学与技术学院 南京211816 
王静 南京工业大学计算机科学与技术学院 南京211816 
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中文摘要:
      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。
英文摘要:
      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.
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