Fusion of Visual and Audio Signals for Wildlife Surveillance

Abstract

Wildlife-vehicle collision (WVC) has been a significant threat to endangered species in Malaysia. Due to excessive development, tropical rainforests and their inhabitants have been edged towards extinction. Road buildings and other linear infrastructures, for instance, have caused forest destruction and forced wild animals to come out from their natural habitats to compete for resources with the human-beings. In Malaysia, much precious wildlife have been lost due to road accidents. Road signs and warning lights have been set up near wildlife crossing, but these do not help much. In this paper, we aim to propose a wildlife surveillance mechanism to detect the existence of wildlife near roadways using visual and audio input. Machine learning classifiers, including Convolution Neural Network (CNN), Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naive Bayes, are adopted in the study. We focus on five types of the most frequently occurring wildlife on the roads: elephants, tapirs, Malayan bears, tigers, and wild boars. Experimental results demonstrate that a good accuracy as high as 99% can be achieved using the proposed approach. On the other hand, the Naïve Bayes classifier ranks the lowest in performance with an accuracy value only up to 86%.

Keywords

• deep learning
• fusion
• machine learning
• wildlife surveillance