Audio surveillance of roads using deep learning and autoencoder-based sample weight initialization

Abstract

Road safety has always been a major concern, where a variety of competences is involved, ranging from government and local authorities, medical caregivers and other service provides. Prompt intervention in emergency cases is one of the key factors to minimize damages. Therefore, real-time surveillance is proposed as an efficient means to detect problems on roads. Video surveillance alone is not enough to detect serious accidents, since any hazardous behavior on the road may be confused with an accident, which may lead to many wrong alarms. Instead, audio processing has the potential to recognize sounds coming from different sources, such as crashes, tire skidding, harsh braking, etc. Since a few years, deep learning has become the state of the art for audio events detection. However, the usual dominance of absence of events in road surveillance would make a bias in the training process. Therefore, a novel method to initialize the neural network's weights using an autoencoder trained only on event-related data is used to balance the data distribution.