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.

A Geometric Convolutional Neural Network for 3D Object Detection We propose a method for accurate 3D vehicle detection based on geometric deep neural networks. From only a single RGB image, the framework is able to recover the 3D positions and predict 3D bounding boxes. In particular, the algorithm leverages single image depth estimation and semantic segmentation to produce 3D point cloud for specific objects. By geometrically constraining the object dimensions, an accurate and stable 3D bounding box which tightly fits into the real object can be estimated. We verify the effectiveness and robustness of our method by comparing with other recent stateofart methods on the challenging KITTI 3D benchmark dataset as well as synthetic Virtual KITTI dataset. Without requiring ground truth 3D labels, our method is able to produce competitive and robust performance in 3D scene understanding and detection.