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Journal of Electrical and Computer Engineering
Volume 2018, Article ID 5381962, 9 pages
Research Article

Target Tracking via Particle Filter and Convolutional Network

1College of Automation, Harbin Engineering University, Harbin, China
2College of Electrical and Information Engineering, Heilongjiang Institute of Technology, Harbin, China

Correspondence should be addressed to Hongxia Chu; moc.361@0240xhc

Received 3 June 2017; Revised 17 August 2017; Accepted 14 November 2017; Published 9 January 2018

Academic Editor: Tongliang Liu

Copyright © 2018 Hongxia Chu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a more effective tracking algorithm which can work robustly in a complex scene such as illumination, appearance change, and partial occlusion. The algorithm is based on an improved particle filter which used the efficient design of observation model. Predefined convolutional filters are used to extract the high-order features. The global representation is generated by combining local features without changing their structures and space arrangements. It not only increases the feature invariance, but also maintains the specificity. The extracted feature from convolution network is introduced into particle filter algorithm. The observation model is constructed by fusing the color feature of the target and a set of features from templates which are extracted by convolutional networks without training in our paper. It is fused with the features extracted from convolutional network for tracking. In the process of tracking, the template is updated in real time, and then the robustness of the algorithm is improved. Experiments show that the algorithm can achieve an ideal tracking effect when the targets are in a complex environment.