[1]马媛媛,杨小军.基于多层深度特征的尺度相关粒子滤波算法[J].计算机技术与发展,2021,31(06):169-174.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 030]
　MA Yuan-yuan,YANG Xiao-jun.A Scale Dependent Particle Filter Algorithm Based onMulti-layer Depth Characteristics[J].,2021,31(06):169-174.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 030]
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基于多层深度特征的尺度相关粒子滤波算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年06期
页码:
169-174
栏目:
应用前沿与综合
出版日期:
2021-06-10

文章信息/Info

Title:
A Scale Dependent Particle Filter Algorithm Based onMulti-layer Depth Characteristics
文章编号:
1673-629X(2021)06-0169-06
作者:
马媛媛; 杨小军
长安大学 信息工程学院,陕西 西安 710064
Author(s):
MA Yuan-yuan; YANG Xiao-jun
School of Information Engineering,Chang’an University,Xi’an 710064,China
关键词:
目标跟踪; 粒子滤波; 相关滤波; 尺度变化; 卷积神经网络
Keywords:
target tracking; particle filtering; correlation filtering; scale change; convolution neural network
分类号:
TP301. 6
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 06. 030
摘要:
针对常见的视频目标跟踪算法难以适应形变以及遮挡等一些干扰场景,该文提出了一种基于多层深度特征的尺度相关粒子滤波跟踪方法。 与现有的基于相关滤波器和粒子滤波器的跟踪方法相比,该算法具有许多优点。 首先,利用区分尺度空间的跟踪器,考虑了目标对象的尺度变化,在对目标尺度估计方面表现良好。 其次,通过卷积神经网络提取目标特征,能够处理目标较大变化和防止跟踪器漂移。 最后,通过尺度相关滤波器将采样的粒子引导至目标状态分布模式,与传统的粒子滤波算法相比,该算法能够用较少的粒子有效地覆盖目标状态,具有鲁棒跟踪性能和较低的计算成本。 通过在具有挑战性的基准数据集上的大量实验表明,该跟踪算法与现有的一些跟踪算法相比具有良好的性能。
Abstract:
In view of the difficulty of common video target tracking algorithms to adapt to some disturbing scenes such as deformation and occlusion,we propose a scale-dependent particle filter tracking method based on multi-layer depth characteristics. Compared with the existing tracking methods based on correlation filter and particle filter,the proposed algorithm has many advantages. Firstly,by using the scale-space discriminating tracker,the scale variation of the target object is taken into account,which performs well in the estimation of the target scale. Secondly,by using the convolutional neural network to extract the target features,the large change of the target can be processed and the tracker drift can be prevented. Finally,the sampled particles are guided to the target state distribution mode by the scale correlation filter. Compared with the traditional particle filter algorithm,the proposed algorithm can effectively cover the target state with fewer particles, and has robust tracking performance and lower computational cost. A large number of experiments on challenging benchmark data sets show that the proposed algorithm has better performance than some existing tracking algorithms.

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更新日期/Last Update: 2021-06-10