Combination of Acoustic and Vibration Sensor Data Using Support Vector Machines and One-Versus-All Technique Data Fusion for Detecting Objects

Abstract

This paper aims to create a device that will be able to detect the presence of an object and classify the object into human, animal, or vehicle by using the information obtained from acoustic and seismic signals. The specific objectives are to develop a hardware device based from Raspberry Pi Minicomputer with seismic and acoustic sensors and transmit sensor signals to a computer for feature extraction and data fusion, to develop a software using Python, MATLAB, and use Data Fusion with the use of Support Vector Machine with One-Versus-All technique, to accurately classify the object into human, animal (canine), or vehicle, to use statistical treatment using multi-class confusion matrix to determine the F-score or accuracy of the classifiers, as an aid for answering the formulated hypotheses. In the testing phase, blind test was performed for the classifiers, using different gathered samples. The F-score of the human, animal, and vehicle classifiers were, respectively, 93.549%, 98.305%, and 100%. The researchers recommend a ground-mounted seismic sensor for comparison of its F-score contribution with the used seismic sensor. Training the SVM models with different parameters could also lead to potential increase in accuracy, such as the number of k-fold cross validations. SVM can as well be compared to other classifier models.