Detecting Sounds of Interest in Roads with Deep Networks

Monitoring of public and private places is of great importance for security of people and is usually done by means of surveillance cameras. In this paper we propose an approach for monitoring of roads, to detect car crashes and tire skidding, based on the analysis of sound signals, which can complement or, in some cases, substitute video analytic systems. The system that we propose employs a MobileNet deep architecture, designed to efficiently run on embedded appliances and be deployed on distributed systems for road monitoring. We designed a recognition system based on analysis of audio frames and tested it on the publicly available MIVIA road events data set. The performance results that we achieved (recognition rate higher than 99%) are higher than existing methods, demonstrating that the proposed approach can be deployed on embedded devices in a distributed surveillance system.