Vehicle Detection Using Aerial Images in Disaster Situations

In disaster situations, it is necessary to rapidly determine the locations of traffic jams and abandoned vehicles to find traffic routes so that rescue activities can be carried out efficiently. However, it takes time to recognize vehicles and estimate their positions. The purpose of our study is to rapidly detect vehicles and their positions in the case of disasters. We propose a method of vehicle recognition and position estimation using an aerial image taken from a helicopter and a road map. Although such images can be taken locally, occlusion occurs when buildings are reflected on the road. For vehicle recognition, we use shadow correction, asphalt removal by machine learning, and shape analysis. In addition, we remove buildings to solve the problem of occlusion. First, we adjust the color of the aerial image by shadow correction. Then, we remove areas of asphalt and buildings on the road, and we extract vehicle areas by using their shape features. To estimate the positions of vehicles, we project the road map on the aerial image by a projective transformation. We extract the road area from the aerial image by the projection before vehicle recognition, thus increasing the efficiency of the process. Using our method, we successfully detected most vehicles with owing to their different colors from the asphalt. Furthermore, we marked the positions of the vehicles on the road map. We thus demonstrated the possibility of the rapid detection of vehicles from aerial images in disaster situations.