3D Detection and Pose Estimation of Vehicle in Cooperative Vehicle Infrastructure System

Three-dimensional (3D) object detection is of great significance for avoiding collisions between vehicles and obstacles in autonomous driving. In particular, the recent 3D object detection methods based on supervised learning are widely studied to achieve excellent performance. However, the 3D labels for training in such methods are expensive and often difficult to be collected. To solve this issue, we propose a monocular 3D vehicle detection method. First, we propose a general mathematical K-means-like method for clustering arbitrary object contours into linear equations. Second, the position, orientation and dimensions of the vehicle can be estimated by applying K-means-like method without the need for 3D labels in the contour of the vehicle. Finally, given the 2D object detection, we maximize a posterior probability of vehicle position, orientation and dimensions to improve the accuracy of the 3D object detection based on the results of K-means-like method. We evaluate the proposed algorithm on the dataset collected by the vehicle-side and road-side cameras in the cooperative vehicle infrastructure system (CVIS). Compared with the state-of-art Deep3DBox and SMOKE methods, the evaluated results show that the detection accuracy of 3D object of our method is 1.4% higher than that of Deep3DBox in the vehicle-side system, while for the road-side camera, the proposed method has 3.86% and 4.37% higher accuracy than Deep3DBox and SMOKE, respectively. Thus, the proposed method can be seen as an effective 3D object detection method in the intelligent transportation system and CVIS.