In this paper, we will introduce our object detection, localization and tracking system for smart mobility applications like traffic road and railway environment. Firstly, an object detection and tracking approach was firstly carried out within two deep learning approaches: You Only Look Once (YOLO) V3 and Single Shot Detector (SSD). A comparison between the two methods allows us to identify their applicability in the traffic environment. Both the performances in road and in railway environments were evaluated. Secondly, object distance estimation based on Monodepth algorithm was developed. This model is trained on stereo images dataset but its inference uses monocular images. As the output data, we have a disparity map that we combine with the output of object detection. To validate our approach, we have tested two models with different backbones including VGG and ResNet used with two datasets : Cityscape and KITTI. As the last step of our approach, we have developed a new method-based SSD to analyse the behavior of pedestrian and vehicle by tracking their movements even in case of no detection on some images of a sequence. We have developed an algorithm based on the coordinates of the output bounding boxes of the SSD algorithm. The objective is to determine if the trajectory of a pedestrian or vehicle can lead to a dangerous situations. The whole of development is tested in real vehicle traffic conditions in Rouen city center, and with videos taken by embedded cameras along the Rouen tramway.

In a railway infrastructure, train geographic location (e.g., GPS) must be strengthened to adapt to the network topology (i.e., inside or outside the station, straight or curved line, passages through tunnels). Alternative solutions must be proposed to meet this need. Computer vision is one of these disruptive answers to tackle this challenge. Indeed, this technology gives meaning to geographic location by getting closer to human behaviour (i.e., human eye). This paper presents an approach detecting the rails solely by computer vision and the knowledge of certain dimensions of the railway. A case study on rail signalling is also proposed to apply this approach in a safety context.