Road vehicle recognition and classification using magnetic field measurement

This dissertation presents a road vehicle detection approach for intelligent transportation systems. This approach uses a roadside-installed, low-cost magnetic sensor and associated data collection system. The system measures magnetic field changes to count, detect, and classify passing vehicles into a number of vehicle types. We compare each vehicle using dynamic time warping (DTW), then extend Mel-Frequency Cepstral Coefficients to analyse the vehicles’ magnetic signals and extract them as vehicle features using the representations of cepstrum, frame energy, and gap cepstrum of magnetic signals. There are three directions (X-axis, Y-axis, and Z-axis directions) in the earth’s magnetic field. We design one- (X-axis direction) and three-dimensional (i.e. X-axis, Y-axis, and Z-axis direction) map algorithms using Vector Quantisation to classify the vehicle magnetic features according to four typical vehicle types for the Australian suburbs: sedan, van, truck, and bus. We also compared experimental results between these two methods. Results show that our approach achieves a high level of accuracy for vehicle detection and classification. In the end, we found that filtering raw magnetic measurement signals can significantly influence vehicle recognition accuracy. Compared with the one-dimensional map, we reached the highest accuracy of vehicle classification in our test data using the three-dimensional map.

Author dc:creator

• Chen, Xiao

• signal processing
• vehicle classification
• traffic model
• magnetic sensing
• intelligent transportation systems

Statement dc:rights

• info:eu-repo/semantics/openAccess
• The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
• au.edu.uts.lib/ppc

Language dc:language.iso

en_AU

Handle dc:identifier.uri

http://hdl.handle.net/10453/129398

OAI identifier oai:identifier

oai:opus.lib.uts.edu.au:10453/129398