Camera Calibration for Tracked Vehicles Augmented Reality Applications

Sobel, Dawid; Jędrasiak, Karol; Daniec, Krzysztof; Wrona, Józef; Jurgaś, Piotr; Nawrat, Aleksander

doi:10.1007/978-3-319-04624-2_8

Cited by 20 publications

(2 citation statements)

References 17 publications

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“…At the same time, studies conducted in the work D. Sobel, K. Jędrasiak, K. Daniec, J. Wrona, P. Jurgaś and A.M. Nawrat [7], in particular, related to evaluating the effectiveness of AR to improve educational experience among abstract or technical courses showed excellent results. "Importance of the study is associated with research efforts performed to evaluate effectiveness of AR on improving learning experiences among abstract or technical courses.…”

Section: Related Workmentioning

confidence: 98%

Training Beginners and Experienced Drivers using mobile-based Virtual and Augmented Reality

Tomchinskaya

Shaposhnikova

Dudakov

2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

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A developed by authors application for real-time visualization of augmented and virtual reality is considered, which enables to simulate the traffic accidents on a mobile platform with the Android operating system. The application is developed on the basis of the Unity 3D and is able to visualize various types of road accidents and also link them to a terrain, different conditions of the urban environment, and map of the accident statistics. The system has an archive of videos for the accident 3D-scenes. The application interface and implementation features are considered. The subsystems of virtual and augmented reality are described in detail with examples. This application may be used to train novice drivers, learn the rules of the road, and inform experienced drivers about new road junctions. In the system environment, the distribution of the number of traffic violations depending on the driving experience for three groups (no experience, less than 3 years, more than 3 years) was investigated. The study of the usefulness of the system on the Likert scale on 80 cadets of driving schools is presented.

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Section: Related Workmentioning

confidence: 98%

Training Beginners and Experienced Drivers using mobile-based Virtual and Augmented Reality

Tomchinskaya

Shaposhnikova

Dudakov

2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

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“…For every frame of video stream, virtual objects have to be placed in a correct position and orientation and rendered again. This is a complex and time consuming process [15].…”

Section: Introductionmentioning

confidence: 99%

Augmented Reality Based Traffic Sign Recognition for Improved Driving Safety

Abdi

Meddeb

Abdallah

2015

Lecture Notes in Computer Science

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Abstract. In recent years, automotive active safety systems have become increasingly common in road vehicles since they provide an opportunity to significantly reduce traffic fatalities by active vehicle control. Augmented Reality (AR) applications can enhance intelligent transportation systems by superimposing surrounding traffic information on the users view and keep drivers and pedestrians view on roads. However, due to the existence of a complex environment such as weather conditions, illuminations and geometric distortions, Traffic Sign Recognition(TSR) systems has always been considered as a challenging task. The aim of this paper is to evaluate the effectiveness of AR cues in improving driving safety by deploying an on-board camera-based driver alert system against approaching traffic signs such as stop, speed limit, unique, danger signs, etc. A new approach is presented for marker-less AR-TSR system that superimposes augmented virtual objects onto a real scene under all types of driving situations including unfavorable weather conditions. Our method is composed of both online and offline stages. An intrinsic camera parameter change depending on the zoom values is calibrated. A Haar-like feature with Adaboost has been used to train a Haar detector in the offline stage. Extrinsic camera parameters are then estimated based on homography method in the online stage. With the complete set of camera parameters, virtual objects can be coherently inserted into the video sequence captured by the camera so that synthetic traffic signs may be added to increase safety.

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