Next Article in Journal
Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms
Previous Article in Journal
Source Coding Options to Improve HEVC Video Streaming in Vehicular Networks
Open AccessArticle

Cross-Voting SVM Method for Multiple Vehicle Classification in Wireless Sensor Networks

College of Artificial Intelligence, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(9), 3108; https://doi.org/10.3390/s18093108
Received: 10 August 2018 / Revised: 5 September 2018 / Accepted: 11 September 2018 / Published: 14 September 2018
(This article belongs to the Section Sensor Networks)
A novel multi-class classification method named the voting-cross support vector machine (SVM) method was proposed in this study, for classifying vehicle targets in wireless sensor networks. The advantages and disadvantages of available methods were summarized, after a comparative analysis of commonly used multi-objective classification algorithms. To improve the classification accuracy of multi-class classification and ensure the low complexity of the algorithm for engineering implementation on wireless sensor network (WSN) nodes, a framework was proposed for cross-matching and voting on the category to which the vehicle belongs after combining the advantages of the directed acyclic graph SVM (DAGSVM) method and binary-tree SVM method. The SVM classifier was selected as the basis two-class classifier in the framework, after comparing the classification performance of several commonly used methods. We utilized datasets acquired from a real-world experiment to validate the proposed method. The calculated results demonstrated that the cross-voting SVM method could effectively increase the classification accuracy for the classification of multiple vehicle targets, with a limited increase in the algorithm complexity. The application of the cross-voting SVM method effectively improved the target classification accuracy (by approximately 7%), compared with the DAGSVM method and the binary-tree SVM method, whereas time consumption decreased by approximately 70% compared to the DAGSVM method. View Full-Text
Keywords: multi-class classification; cross-voting SVM method; vehicle classification; wireless sensor networks (WSNs) multi-class classification; cross-voting SVM method; vehicle classification; wireless sensor networks (WSNs)
Show Figures

Figure 1

MDPI and ACS Style

Zhang, H.; Pan, Z. Cross-Voting SVM Method for Multiple Vehicle Classification in Wireless Sensor Networks. Sensors 2018, 18, 3108.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop