Towards A Vision-Based Forward Collision Warning System

2020 ◽  
Author(s):  
S. M. Saleh ◽  
H. M. Saleh ◽  
N. T. Toure ◽  
Wolfram Hardt
Keyword(s):  
Real Time ◽  
Distance Estimation ◽  
Warning System ◽  
Region Of Interest ◽  
Depth Map ◽  
Threshold Value ◽  
Driving Safety ◽  
Annual Number ◽  
Robust Solution ◽  
On The Road

The annual number of road deaths is still increasing, especially in less developed and developing countries. Road accidents are the 5th cause of death and the leading reason for death among young people between 5 and 29 years of age in 2030. In this study, a robust solution is implemented by integrating object recognition with distance estimation to maximize driving safety. The proposed system will be able to detect common objects within the region of interest on the road and estimate how far these objects are from the camera position. The system will trigger an alarm to attract the driver’s attention in real time when the distance to one of the detected objects is less than a predefined threshold value. In this work YOLO (You Only Look Once) approach is used to detect the objects in real time and the properties of the depth map based on deep learning is applied to estimate the distance at a given point

Improved Position and Attitude Determination Method for Monocular Vision in Vehicle Collision Warning System

2016 ◽  
Vol 30 (07) ◽  
pp. 1655019 ◽  
Author(s):  
Lijuan Qin ◽  
Ting Wang ◽  
Yulan Hu ◽  
Chen Yao
Keyword(s):  
Real Time ◽  
Solution Method ◽  
Warning System ◽  
Time Characteristic ◽  
Monocular Vision ◽  
Theoretical Research ◽  
Driving Safety ◽  
License Plate ◽  
Collision Warning ◽  
Vehicle Collision

Vehicle collision warning system can determine the relative distance and speed between target vehicle and the front vehicle by monocular vision positioning technique from automobile license plate image captured by camera so as to judge danger level and remind the driver to make appropriate action and avoid vehicle collision timely. Study on the positioning technology of this system aims to help the driver to judge and improve driving safety. Thus, the system has a broad application prospect. The research content of this paper could enrich and supply PNL visual locating method, endowing with significance of theoretical research. The paper proposes an improved vehicle measuring method based on monocular vision for vehicle license plate. This method combines the characteristics of fast speed for analytical solution method and high positioning accuracy for iterative solution method, therefore has a high robustness and overcomes the multi-solution problem of P3P iterative method. The simulation experiments show that localization precision of the improved positioning method has been improved greatly as compared with P4L method. At the same time, the real-time characteristic of collision avoidance warning system with improved visual locating method has been improved a lot, and the new location algorithm has good performance in real-time characteristic, which greatly improve the processing ability of the system for images.

scholarly journals Real-Time Video Processing using Contour Numbers and Angles for Non-urban Road Marker Classification

2018 ◽  
Vol 8 (4) ◽  
pp. 2540
Author(s):  
Zamani Md Sani ◽  
Hadhrami Abd Ghani ◽  
Rosli Besar ◽  
Azizul Azizan ◽  
Hafiza Abas
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Processing Time ◽  
Region Of Interest ◽  
Adaptive Smoothing ◽  
Urban Road ◽  
The Road ◽  
Road Users ◽  
Average Accuracy ◽  
On The Road

Road users make vital decisions to safely maneuver their vehicles based on the road markers, which need to be correctly classified. The road markers classification is significantly important especially for the autonomous car technology. The current problems of extensive processing time and relatively lower average accuracy when classifying up to five types of road markers are addressed in this paper. Two novel real time video processing methods are proposed by extracting two formulated features namely the contour number, , and angle, 𝜃 to classify the road markers. Initially, the camera position is calibrated to obtain the best Field of View (FOV) for identifying a customized Region of Interest (ROI). An adaptive smoothing algorithm is performed on the ROI before the contours of the road markers and the corresponding two features are determined. It is observed that the achievable accuracy of the proposed methods at several non-urban road scenarios is approximately 96% and the processing time per frame is significantly reduced when the video resolution increases as compared to that of the existing approach.

Vocal tract cross-distance estimation from real-time MRI using region-of-interest analysis

2013 ◽  
Author(s):  
Adam Lammert ◽  
Vikram Ramanarayanan ◽  
Michael Proctor ◽  
Shrikanth Narayanan
Keyword(s):  
Real Time ◽  
Vocal Tract ◽  
Distance Estimation ◽  
Region Of Interest

Safety Monitoring and Warning System for Subway Construction Workers using Wearable Technology

2019 ◽  
Vol 13 ◽  
Author(s):  
Jun-hua Chen ◽  
Da-hu Wang ◽  
Cun-yuan Sun
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Early Warning ◽  
Warning System ◽  
Safety Monitoring ◽  
Wearable Technology ◽  
Construction Workers ◽  
Great Success ◽  
Subway Construction ◽  
Monitoring And Early Warning

Objective: This study focused on the application of wearable technology in the safety monitoring and early warning for subway construction workers. Methods: With the help of real-time video surveillance and RFID positioning which was applied in the construction has realized the real-time monitoring and early warning of on-site construction to a certain extent, but there are still some problems. Real-time video surveillance technology relies on monitoring equipment, while the location of the equipment is fixed, so it is difficult to meet the full coverage of the construction site. However, wearable technologies can solve this problem, they have outstanding performance in collecting workers’ information, especially physiological state data and positioning data. Meanwhile, wearable technology has no impact on work and is not subject to the inference of dynamic environment. Results and conclusion: The first time the system applied to subway construction was a great success. During the construction of the station, the number of occurrences of safety warnings was 43 times, but the number of occurrences of safety accidents was 0, which showed that the safety monitoring and early warning system played a significant role and worked out perfectly.

scholarly journals A Residual Network and FPGA Based Real-Time Depth Map Enhancement System

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 546
Author(s):  
Zhenni Li ◽  
Haoyi Sun ◽  
Yuliang Gao ◽  
Jiao Wang
Keyword(s):  
Real Time ◽  
Super Resolution ◽  
Depth Map ◽  
Acquisition System ◽  
Depth Image ◽  
Fpga Design ◽  
Depth Sensing ◽  
Residual Network ◽  
Real Time Processing ◽  
Depth Maps

Depth maps obtained through sensors are often unsatisfactory because of their low-resolution and noise interference. In this paper, we propose a real-time depth map enhancement system based on a residual network which uses dual channels to process depth maps and intensity maps respectively and cancels the preprocessing process, and the algorithm proposed can achieve real-time processing speed at more than 30 fps. Furthermore, the FPGA design and implementation for depth sensing is also introduced. In this FPGA design, intensity image and depth image are captured by the dual-camera synchronous acquisition system as the input of neural network. Experiments on various depth map restoration shows our algorithms has better performance than existing LRMC, DE-CNN and DDTF algorithms on standard datasets and has a better depth map super-resolution, and our FPGA completed the test of the system to ensure that the data throughput of the USB 3.0 interface of the acquisition system is stable at 226 Mbps, and support dual-camera to work at full speed, that is, 54 fps@ (1280 × 960 + 328 × 248 × 3).

scholarly journals An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

2021 ◽  
Vol 13 (12) ◽  
pp. 2259
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Qing Zhao ◽  
Zihan Peng ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
Sampling Rate ◽  
Threshold Value ◽  
Deformation Monitoring ◽  
Success Rates ◽  
Multipath Mitigation ◽  
Mode Decomposition ◽  
Observation Noise ◽  
Original Observation ◽  
Better Than

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

scholarly journals An Augmented Warning System for Pedestrians: User Interface Design and Algorithm Development

2021 ◽  
Vol 11 (16) ◽  
pp. 7197
Author(s):  
Yourui Tong ◽  
Bochen Jia ◽  
Shan Bao
Keyword(s):  
User Interface ◽  
Real Time ◽  
Interface Design ◽  
Situational Awareness ◽  
Warning System ◽  
User Interface Design ◽  
Warning Systems ◽  
Projection Algorithms ◽  
Time Projection ◽  
Timely Warning

Warning pedestrians of oncoming vehicles is critical to improving pedestrian safety. Due to the limitations of a pedestrian’s carrying capacity, it is crucial to find an effective solution to provide warnings to pedestrians in real-time. Limited numbers of studies focused on warning pedestrians of oncoming vehicles. Few studies focused on developing visual warning systems for pedestrians through wearable devices. In this study, various real-time projection algorithms were developed to provide accurate warning information in a timely way. A pilot study was completed to test the algorithm and the user interface design. The projection algorithms can update the warning information and correctly fit it into an easy-to-understand interface. By using this system, timely warning information can be sent to those pedestrians who have lower situational awareness or obstructed view to protect them from potential collisions. It can work well when the sightline is blocked by obstructions.

scholarly journals Stochastic-Depth Ambient Occlusion

2021 ◽  
Vol 4 (1) ◽  
pp. 1-15
Author(s):  
Jop Vermeer ◽  
Leonardo Scandolo ◽  
Elmar Eisemann
Keyword(s):  
Real Time ◽  
High Performance ◽  
Depth Map ◽  
Visual Quality ◽  
Ambient Light ◽  
Missing Information ◽  
Time Performance ◽  
Ambient Occlusion ◽  
Depth Buffer ◽  
Screen Space

Ambient occlusion (AO) is a popular rendering technique that enhances depth perception and realism by darkening locations that are less exposed to ambient light (e.g., corners and creases). In real-time applications, screen-space variants, relying on the depth buffer, are used due to their high performance and good visual quality. However, these only take visible surfaces into account, resulting in inconsistencies, especially during motion. Stochastic-Depth Ambient Occlusion is a novel AO algorithm that accounts for occluded geometry by relying on a stochastic depth map, capturing multiple scene layers per pixel at random. Hereby, we efficiently gather missing information in order to improve upon the accuracy and spatial stability of conventional screen-space approximations, while maintaining real-time performance. Our approach integrates well into existing rendering pipelines and improves the robustness of many different AO techniques, including multi-view solutions.

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