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

scholarly journals A State-of-the-Art Review on Mapping and Localization of Mobile Robots Using Omnidirectional Vision Sensors

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
L. Payá ◽  
A. Gil ◽  
O. Reinoso
Keyword(s):  
Computer Vision ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Vehicles ◽  
Scientific Community ◽  
Mobile Robotics ◽  
State Of The Art ◽  
Omnidirectional Vision ◽  
Vision Sensors ◽  
Source Of Information

Nowadays, the field of mobile robotics is experiencing a quick evolution, and a variety of autonomous vehicles is available to solve different tasks. The advances in computer vision have led to a substantial increase in the use of cameras as the main sensors in mobile robots. They can be used as the only source of information or in combination with other sensors such as odometry or laser. Among vision systems, omnidirectional sensors stand out due to the richness of the information they provide the robot with, and an increasing number of works about them have been published over the last few years, leading to a wide variety of frameworks. In this review, some of the most important works are analysed. One of the key problems the scientific community is addressing currently is the improvement of the autonomy of mobile robots. To this end, building robust models of the environment and solving the localization and navigation problems are three important abilities that any mobile robot must have. Taking it into account, the review concentrates on these problems; how researchers have addressed them by means of omnidirectional vision; the main frameworks they have proposed; and how they have evolved in recent years.

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

2020 ◽  
Author(s):  
Joel Janai ◽  
Fatma Güney ◽  
Aseem Behl ◽  
Andreas Geiger
Keyword(s):  
Computer Vision ◽  
Autonomous Vehicles ◽  
State Of The Art

Computer Vision and robotics in postal automation

1999 ◽  
Vol 18 (3-4) ◽  
pp. 265-273
Author(s):  
Giovanni B. Garibotto
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Material Handling ◽  
State Of The Art ◽  
Short Description ◽  
The Other ◽  
Functional Requirements ◽  
Postal Automation ◽  
And Robotics

The paper is intended to provide an overview of advanced robotic technologies within the context of Postal Automation services. The main functional requirements of the application are briefly referred, as well as the state of the art and new emerging solutions. Image Processing and Pattern Recognition have always played a fundamental role in Address Interpretation and Mail sorting and the new challenging objective is now off-line handwritten cursive recognition, in order to be able to handle all kind of addresses in a uniform way. On the other hand, advanced electromechanical and robotic solutions are extremely important to solve the problems of mail storage, transportation and distribution, as well as for material handling and logistics. Finally a short description of new services of Postal Automation is referred, by considering new emerging services of hybrid mail and paper to electronic conversion.

scholarly journals A Benchmark and Evaluation of Non-Rigid Structure from Motion

2020 ◽  
Author(s):  
Sebastian Hoppe Nesgaard Jensen ◽  
Mads Emil Brix Doest ◽  
Henrik Aanæs ◽  
Alessio Del Bue
Keyword(s):  
Computer Vision ◽  
Structure From Motion ◽  
State Of The Art ◽  
The State ◽  
Quality Data ◽  
Data Set ◽  
Rigid Structure ◽  
Public Data ◽  
3D Information ◽  
Further Development

AbstractNon-rigid structure from motion (nrsfm), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of nrsfm, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse nrsfm. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Understanding the state of the Art in Animal detection and classification using computer vision technologies

2021 ◽  
Author(s):  
Gabriel S. Ferrante ◽  
Felipe M. Rodrigues ◽  
Fernando R. H. Andrade ◽  
Rudinei Goularte ◽  
Rodolfo I. Meneguette
Keyword(s):  
Computer Vision ◽  
State Of The Art ◽  
The State ◽  
Animal Detection

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 Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

2021 ◽  
Author(s):  
Phongsathorn Kittiworapanya ◽  
Kitsuchart Pasupa ◽  
Peter Auer
Keyword(s):  
Computer Vision ◽  
Particle Size ◽  
Deep Learning ◽  
Waste Management ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Input Image ◽  
Size Estimation ◽  
Waste Particles ◽  
Set Up

<div>We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.<br></div>

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.

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