Audio Surveillance of Road Traffic: An Approach Based on Anomaly Detection and Interval Type-2 Fuzzy Sets

Surveillance systems are increasingly exploiting multimodal information for improved effectiveness. This paper presents an audio event detection method for road traffic surveillance, combining generative deep autoencoders and fuzzy modelling to perform anomaly detection. Baseline deep autoencoders are used to compute the reconstruction error of each audio segment, which provides a primary estimation of outlierness. To account for the uncertainty associated to this decision-making step, an interval type-2 fuzzy membership function composed of an optimistic/upper component and a pessimistic/lower component is used. The final class attribution employs a probabilistic method for interval comparison. Evaluation results obtained after defuzzification show that, with a careful parameter setting, the proposed membership function effectively improves the performance of the baseline autoencoder, and performs better than the state-of-the-art one-class SVM in anomaly detection.