Pose Recognition for Dense Vehicles under Complex Street Scenario Locating vehicles in the surrounding environment and distinguishing their poses are crucial in autonomous driving systems. An intelligent transportation system expects a visionbased object detector not only to detect and classify vehicles but also to recognize the poses of them in estimating their future movement direction. However, most of the current object detection algorithms only classify different classes of vehicles; and there are rare studies on classifying different poses within the same class of vehicles. In addition, the currently available datasets have no annotation data that are specific to different poses of intraclass vehicles. To achieve the vehicles pose recognition, we build a vehicle pose dataset based on the Cityscapes dataset. Furthermore, we also improve the network model of the YOLO v2 detector to support simultaneous vehicle detection and pose classification with an improvement of 10.71% mean average precision (mAP) comparing to the default network which runs in realtime with 29 FPS.

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.