Estimating the 2D Static Map Based on Moving Stereo Camera

Saleh, Shadi; Khwandah, Sinan; Hardt, Wolf‐Dietrich; Hilbrich, Marcus; Lazaridis, Pavlos I.

doi:10.23919/iconac.2018.8749004

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…in the field of developing autonomous driving cars computer vision has become a key component, due the advances in visual environment perception like object classification, detection, segmentation and distance estimation Techniques .Researchers mainly focus on object classification, detection and segmentation [1,2,3], beside a lot of efforts on improving the accuracy of visual perception crucial information for cars to avoid collisions, adjust its speed for safety driving can be provided through distances estimating between camera sensors [25,26,27] and recognized objects (e.g. cyclists , pedestrians ,cars) and not only by recognizing the objects on the way.…”

Section: Introductionmentioning

confidence: 99%

Collision Avoidance by Identifying Risks for Detected Objects in Autonomous Vehicles

Hasn

Ali²

2021

ESS

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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 roads

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Section: Introductionmentioning

confidence: 99%

Collision Avoidance by Identifying Risks for Detected Objects in Autonomous Vehicles

Hasn

Ali²

2021

ESS

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“…This problem in stereo vision sensors is resolved by computing a disparity image from the stereo image pair to extract depth information as described [8]. The alternative solution for obtaining the depth would be to employ range sensors such as Lidar or radar.…”

Section: Introductionmentioning

confidence: 99%

Real-time 3D Perception of Scene with Monocular Camera

Saleh

Manoharan²,

Hardt³

2020

ESS

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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.

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Robust Collision Warning System based on Multi Objects Distance Estimation

Saleh

Toure

et al. 2021

2021 IEEE Concurrent Processes Architectures and Embedded Systems Virtual Conference (COPA)

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Estimating the 2D Static Map Based on Moving Stereo Camera

Cited by 3 publications

References 14 publications

Collision Avoidance by Identifying Risks for Detected Objects in Autonomous Vehicles

Collision Avoidance by Identifying Risks for Detected Objects in Autonomous Vehicles

Real-time 3D Perception of Scene with Monocular Camera

Robust Collision Warning System based on Multi Objects Distance Estimation

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