Spatial-Temporal Correlation 3D Vehicle Detection and Tracking System with Multiple Surveillance Cameras

Compared to 3D object detection using a single camera,multiple cameras can overcome some limitations on field-of-view,occlusion,and low detection confidence.This study employs multiple surveillance cameras and develops a cooperative 3D object detection and tracking framework by incorporating temporal and spatial information.The framework consists of a 3D vehicle detection model,cooperatively spatial-temporal relation scheme,and heuristic camera constellation method.Specifically,the proposed cross-camera association scheme combines the geometric relationship between multiple cameras and objects in corresponding detections.The spatial-temporal method is designed to associate vehicles between different points of view at a single timestamp and fulfill vehicle tracking in the time aspect.The proposed framework is evaluated based on a synthetic cooperative dataset and shows high reliability,where the cooperative perception can recall more than 66%of the trajectory instead of 11%for single-point sensing.This could contribute to full-range surveillance for intelligent transportation systems.