Dynamic Obstacle Tracking Based On HighDefinition Map In Urban Scene The application of HighDefinition map can realize centimeterlevel position in urban scenes, the development of deep learning has made great breakthrough in the point cloud dynamic obstacle recognition. All these technologies make obstacle detection and tracking based on HighDefinition map effectively realize in the modern smart city scene. It is different from the previous obstacle detection and tracking methods based purely on vision the using of HighDefinition map can provide highprecision positioning and reduce the difficulty of point cloud classification. The using of lidar also solves the problem of uptoscale in dynamic detection. In this paper, we put forward a new Multicamera Lidar Point Cloud Map, we complete the map at normal speed on the highway and get a satisfactory result. At the same time, we also find a robust combination of traditional kalman filter, Hungary algorithm and current deep learning to solve dynamic obstacle tracking and detection. The experimental results show that our system can effectively complete the special problem of generating map and target tracking on urban scene.

Deep Learning for RealTime 3D MultiObject Detection, Localisation, and Tracking: Application to Smart Mobility In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account realtime constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detectorbased on YOLOv3 which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 for monocular vision and MADNEt for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in realtime conditions. Object tracking is necessary in order to mitigate the risks of collisions. Unlike traditional tracking approaches which require target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT approach for 3D object tracking. We introduce an extended Kalman filter to better estimate the position of objects. Extensive experiments carried out on KITTI dataset prove that our proposal outperforms stateoftheart approches.