Object Detection for Neighbor Map Construction in an IoV System

Many applications of machine-to-machine (M2M) based intelligent transportation systems highly rely on the accurate estimation of neighbor map, where neighbor map mentions the locations of all nearby vehicles and pedestrians. To build the neighbor map, it usually integrates multiple sensors, such as GPS, odometer, inertial measurement unit (IMU), laser scanners, cameras, and RGB-D cameras. In this paper, we build a M2M framework to estimate the neighbor map and focus on the improvement of vehicle and pedestrian detection of most popular sensors, camera. We propose a novel grid-based object detection approach and deal with cameras on both roadside units and vehicles. It adapts to the environments and achieves high accuracy, and can be used to improve the performance of neighbor map estimation.