On Improving Bounding Box Regression Towards Accurate Object Detection and Tracking In this paper, an improved bounding box regression technique is studied for CNNbased object detection and tracking. The proposed approach uses a CNNbased network to identify and localise objects which are boxed by a region of interest regressor. In many cases, a generated bounding box does not fit tightly to the detected object which leads to inaccurate results for spatial analysis such as geometric warping. In this paper, a technique is designed to improve this situation by means of background modelling, motion analysis, template correlation and Kalman filters. The experimental results suggest that the proposed method outperforms single frame object detectorbased tracking techniques with an improvement of over 80% in the context of surveillance camerabased pedestrian analysis.

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.