[1]WANG Hongxiang,LIU Peizhong,LUO Yanmin,et al.Convolutional neutral network tracking algorithm accelerated by Gaussian kernel function[J].CAAI Transactions on Intelligent Systems,2018,13(3):388-394.[doi:10.11992/tis.201612040]
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Convolutional neutral network tracking algorithm accelerated by Gaussian kernel function

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 3 Page number: 388-394 Column: 学术论文—机器学习 Public date: 2018-05-05
Title:
Convolutional neutral network tracking algorithm accelerated by Gaussian kernel function
Author(s):
WANG Hongxiang1;  LIU Peizhong1;  LUO Yanmin2;  DU Yongzhao1;  CHEN Zhi1
1. College of Engineering, Huaqiao University, Quanzhou 362021, China;
2. College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
Keywords:
visual tracking; deep learning; convolutional neural network (CNN); gauss kernel function; foreground object; background information; template matching; particle filter
CLC:
TP391
DOI:
10.11992/tis.201612040
Abstract:
In view of such defects existing in the depth learning tracking algorithm as lack of training samples, large time consumption, and high complexity, this paper proposed a simplified convolutional neural network tracking algorithm in which training is unnecessary. Moreover, the Gaussian kernel function can be applied to this algorithm to significantly lower the computing time. Firstly, the initial frame target was normalized and clustered to extract a series of initial filter banks; in the tracking process, the background information of the target and the candidate target for the foreground were convoluted; then the simple and abstract features of the target were extracted; finally, all the convolutions of a simple layer were superposed to form a deep-level feature representation. The Gaussian kernel function was used to speed-up the convolution operations; also, the local structural feature information of the target was used to update the filters in every stage of the network; in addition, the tracking was realized by combining the particle filter tracking framework. The experimental results on the CVPR2013 tracking datasets show that the method used in this paper can help avoid the typically cumbersome operational environment of deep learning, overcome local object occlusion and deformation at low resolution, and improve tracking efficiency under a complex background.
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