Real-time 3D Perception of Scene with Monocular Camera

Depth is a vital prerequisite for the fulfillment of various tasks such as perception, navigation, and planning. Estimating depth using only a single image is a challenging task since the analytic mapping is not available between the intensity image and its depth where the features cue of the context is usually absent in the single image. Furthermore, most current researchers rely on the supervised Learning approach to handle depth estimation. Therefore, the demand for recorded ground truth depth is important at the training time, which is actually tricky and costly. This study presents two approaches (unsupervised learning and semi-supervised learning) to learn the depth information using only a single RGB-image. The main objective of depth estimation is to extract a representation of the spatial structure of the environment and to restore the 3D shape and visual appearance of objects in imagery

Monocular visual traffic surveillance: a review

To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined

FARSEC: A Reproducible Framework for Automatic Real-Time Vehicle Speed Estimation Using Traffic Cameras

Estimating the speed of vehicles using traffic cameras is a crucial task for traffic surveillance and management, enabling more optimal traffic flow, improved road safety, and lower environmental impact. Transportation-dependent systems, such as for navigation and logistics, have great potential to benefit from reliable speed estimation. While there is prior research in this area reporting competitive accuracy levels, their solutions lack reproducibility and robustness across different datasets. To address this, we provide a novel framework for automatic real-time vehicle speed calculation, which copes with more diverse data from publicly available traffic cameras to achieve greater robustness. Our model employs novel techniques to estimate the length of road segments via depth map prediction. Additionally, our framework is capable of handling realistic conditions such as camera movements and different video stream inputs automatically. We compare our model to three well-known models in the field using their benchmark datasets. While our model does not set a new state of the art regarding prediction performance, the results are competitive on realistic CCTV videos. At the same time, our end-to-end pipeline offers more consistent results, an easier implementation, and better compatibility. Its modular structure facilitates reproducibility and future improvements

Collision Avoidance by Identifying Risks for Detected Objects in Autonomous Vehicles

We propose a system which will detect objects onour roads, estimate the distance of these object from the cameraand alert the driver if this distance is equal or less than thethreshold value(02meters),and assist the driver and alert him assoon as possible in order for him to take appropriate actions assoon as possible which can avoid any collision or significantlyreduce it. We plan to use state of the arts object detection modelslike YOLO to identify the target object classes and use depthmaps from monocular camera to be give an accurate estimate ofthe distance of the detected object from the camera. one majorrequirement of this system is the real-time behaviour and a highaccuracy for the detected and estimated distance, A secondrequirement is to make the system cheap and easy useablecomparatively to the other existing methods. That is why wedecided to use monocular camera images and depth maps whichmakes the solution cheap and innovative. This project(prototype) provide room for bigger and more complete projectwhich will contribute to the creation of tool which can save livesand improve security on our road

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved