scholarly journals Computer Vision Based Pre-Processing System for Autonomous Vehicles

2021 ◽  
Vol 2 (1) ◽  
Keyword(s):  
Computer Vision ◽  
Autonomous Vehicles ◽  
Processing System

End-to-end imaging system optimization for computer vision in driving automation

2021 ◽  
Keyword(s):  
Computer Vision ◽  
International Symposium ◽  
Autonomous Vehicles ◽  
Fast Track ◽  
Imaging System ◽  
System Optimization ◽  
Electronic Imaging ◽  
End To End

Fast track article for IS&T International Symposium on Electronic Imaging 2021: Autonomous Vehicles and Machines 2021 proceedings.

scholarly journals Computer vision based obstacle detection and target tracking for autonomous vehicles

2021 ◽  
Vol 336 ◽  
pp. 07004
Author(s):  
Ruoyu Fang ◽  
Cheng Cai
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Target Tracking ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Pid Algorithm ◽  
Deep Learning Neural Network

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.

scholarly journals A two-level computer vision-based information processing method for improving the performance of human–machine interaction-aided applications

2020 ◽  
Author(s):  
Osama Alfarraj ◽  
Amr Tolba
Keyword(s):  
Computer Vision ◽  
Information Processing ◽  
Visual Information ◽  
Information Gain ◽  
Processing System ◽  
Human Machine Interaction ◽  
Visual Inputs ◽  
Level Data ◽  
Processing Errors ◽  
Fully Connected

Abstract The computer vision (CV) paradigm is introduced to improve the computational and processing system efficiencies through visual inputs. These visual inputs are processed using sophisticated techniques for improving the reliability of human–machine interactions (HMIs). The processing of visual inputs requires multi-level data computations for achieving application-specific reliability. Therefore, in this paper, a two-level visual information processing (2LVIP) method is introduced to meet the reliability requirements of HMI applications. The 2LVIP method is used for handling both structured and unstructured data through classification learning to extract the maximum gain from the inputs. The introduced method identifies the gain-related features on its first level and optimizes the features to improve information gain. In the second level, the error is reduced through a regression process to stabilize the precision to meet the HMI application demands. The two levels are interoperable and fully connected to achieve better gain and precision through the reduction in information processing errors. The analysis results show that the proposed method achieves 9.42% higher information gain and a 6.51% smaller error under different classification instances compared with conventional methods.

scholarly journals DETECTION OF LANE LINES FOR SELFDRIVING CARS BY USING EFFECTIVE COMPUTER VISION TECHNIQUES WITH OPENCV IN PYTHON

2021 ◽  
pp. 69-72
Author(s):  
Aryan Verma
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Autonomous Vehicles ◽  
Semantic Segmentation ◽  
Visual Features ◽  
Traffic Lights ◽  
Classification Of Images ◽  
Pedestrian Crossing ◽  
Source Of Information

Presently computer vision is amongst the hottest topics in Artificial Intelligence and is being extensively used in Robotics, Detecting Objects, Classification of Images, Autonomous Vehicles & tracking, Semantic Segmentation along with photo correction in various apps. In Self driven cars/ vehicles, vision remains the main source of information for detecting lanes, traffic lights, pedestrian crossing and other visual features. [2]

scholarly journals Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art

2020 ◽  
Vol 12 (1–3) ◽  
pp. 1-308 ◽  
Author(s):  
Joel Janai ◽  
Fatma Güney ◽  
Aseem Behl ◽  
Andreas Geiger
Keyword(s):  
Computer Vision ◽  
Autonomous Vehicles ◽  
State Of The Art

Fast Prediction of Contrast Detection Probability

2020 ◽  
Vol 2020 (16) ◽  
pp. 40-1-40-7
Author(s):  
Robin Jenkin
Keyword(s):  
Computer Vision ◽  
Physical Model ◽  
Detection Probability ◽  
Autonomous Vehicles ◽  
Absolute Error ◽  
Contrast Detection ◽  
Full Calculation ◽  
Gaussian Statistics ◽  
Fast Prediction ◽  
Individual Patch

Contrast detection probability (CDP) is proposed as an IEEE P2020 metric to predict camera performance intended for computer vision tasks for autonomous vehicles. Its calculation involves comparing combinations of pixel values between imaged patches. Computation of CDP for all meaningful combinations of m patches involves approximately 3/2(m2-m).n4 operations, where n is the length of one side of the patch in pixels. This work presents a method to estimate Weber contrast based CDP based on individual patch statistics and thus reduces to computation to approximately 4n2m calculations. For 180 patches of 10×10 pixels this is a reduction of approximately 6500 times and for 180 25×25 pixel patches, approximately 41000. The absolute error in the estimated CDP is less than 0.04 or 5% where the noise is well described by Gaussian statistics. Results are compared for simulated patches between the full calculation and the fast estimate. Basing the estimate of CDP on individual patch statistics, rather than by a pixel-to-pixel comparison facilitates the prediction of CDP values from a physical model of exposure and camera conditions. This allows Weber CDP behavior to be investigated for a wide variety of conditions and leads to the discovery that, for the case where contrast is increased by decreasing the tone value of one patch and therefore increasing noise as contrast increases, there exists a maxima which yields identical Weber CDP values for patches of different nominal contrast. This means Weber CDP is predicting the same detection performance for patches of different contrast.

scholarly journals Technical Note: A respiratory monitoring and processing system based on computer vision: prototype and proof of principle

2016 ◽  
Vol 17 (5) ◽  
pp. 534-541 ◽  
Author(s):  
Nicolas Leduc ◽  
Vincent Atallah ◽  
Patrick Escarmant ◽  
Vincent Vinh-Hung
Keyword(s):  
Computer Vision ◽  
Processing System ◽  
Technical Note ◽  
Respiratory Monitoring ◽  
Proof Of Principle

scholarly journals Implementation of a Sensor Big Data Processing System for Autonomous Vehicles in the C-ITS Environment

2020 ◽  
Vol 10 (21) ◽  
pp. 7858
Author(s):  
Aelee Yoo ◽  
Sooyeon Shin ◽  
Junwon Lee ◽  
Changjoo Moon
Keyword(s):  
Big Data ◽  
Autonomous Vehicles ◽  
Processing System ◽  
Intelligent Transport System ◽  
Local Dynamic ◽  
Intelligent Transport ◽  
The Road ◽  
Road Condition ◽  
Dynamic Map ◽  
Using Data

To provide a service that guarantees driver comfort and safety, a platform utilizing connected car big data is required. This study first aims to design and develop such a platform to improve the function of providing vehicle and road condition information of the previously defined central Local Dynamic Map (LDM). Our platform extends the range of connected car big data collection from OBU (On Board Unit) and CAN to camera, LiDAR, and GPS sensors. By using data of vehicles being driven, the range of roads available for analysis can be expanded, and the road condition determination method can be diversified. Herein, the system was designed and implemented based on the Hadoop ecosystem, i.e., Hadoop, Spark, and Kafka, to collect and store connected car big data. We propose a direction of the cooperative intelligent transport system (C-ITS) development by showing a plan to utilize the platform in the C-ITS environment.

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