A Novel Approach for Emergency Vehicle Detection

Emergency vehicles such as ambulances or police vehicles require special treatment in traffic scenarios. To this end, their detection needs an automated approach, to allow them to scale to large urban areas in the country. Recently, vision-based systems have become a key enabler in traffic management, in this paper we propose a novel approach for emergency vehicle detection, which has a lesser occurrence of false positives and false negatives compared to existing vision-based approaches. The key idea of our approach is to detect the siren on top of the vehicle, rather than the vehicle itself, thereby giving us two main advantages: first, if the vehicle is of any type, it is still detected as an emergency vehicle irrespective of the vehicle shape and second, non-emergency vehicles of similar make and model are not detected. We have created, and made publicly available, a dataset using image searches and traditional enhancement techniques to increase its size. We have then trained the classification layer of the popular YOLO architecture using this enhanced dataset. We obtain a training accuracy of 77.1% for detecting the siren and also show with multiple examples how false positives and false negatives are avoided using this approach. This approach hence provides a reliable methodology for emergency vehicle detection, an important component of traffic management systems as part of the smart cities’ objective.