FisheyeMultiNet: Real-time Multi-task Learning Architecture for
Surround-view Automated Parking System
• Generalized Object Detection on Fisheye Cameras for Autonomous
Driving: Dataset, Representations and Baseline
• SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for
Autonomous Driving
• Feasible Self-Calibration of Larger Field-of-View (FOV) Camera Sensors
for the ADAS
Fisheye based Perception for Autonomous Driving VIYu Huang
Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras
SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras
FisheyeDistanceNet++: Self-Supervised Fisheye Distance Estimation with Self-Attention, Robust Loss Function and Camera View Generalization
An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras
RoadEdgeNet: Road Edge Detection System Using Surround View Camera Images
Fisheye based Perception for Autonomous Driving VIYu Huang
Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras
SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras
FisheyeDistanceNet++: Self-Supervised Fisheye Distance Estimation with Self-Attention, Robust Loss Function and Camera View Generalization
An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras
RoadEdgeNet: Road Edge Detection System Using Surround View Camera Images
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/introduction-to-simultaneous-localization-and-mapping-slam-a-presentation-from-gareth-cross/
Independent game developer (and former technical lead of state estimation at Skydio) Gareth Cross presents the “Introduction to Simultaneous Localization and Mapping (SLAM)” tutorial at the May 2021 Embedded Vision Summit.
This talk provides an introduction to the fundamentals of simultaneous localization and mapping (SLAM). Cross aims to provide foundational knowledge, and viewers are not expected to have any prerequisite experience in the field.
The talk consists of an introduction to the concept of SLAM, as well as practical design considerations in formulating SLAM problems. Visual inertial odometry is introduced as a motivating example of SLAM, and Cross explains how this problem is structured and solved.
Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry...Masaya Kaneko
SfMLearner + KF selectionを提案した"Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM [ICCV19]"を論文読み会で紹介した時の資料です.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sep-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Raghuraman Krishnamoorthi, Software Engineer at Facebook, delivers the presentation "Quantizing Deep Networks for Efficient Inference at the Edge" at the Embedded Vision Alliance's September 2019 Vision Industry and Technology Forum. Krishnamoorthi gives an overview of practical deep neural network quantization techniques and tools.
Fisheye/Omnidirectional View in Autonomous Driving VYu Huang
Road-line detection and 3D reconstruction using fisheye cameras
• Vehicle Re-ID for Surround-view Camera System
• SynDistNet: Self-Supervised Monocular Fisheye Camera Distance
Estimation Synergized with Semantic Segmentation for Autonomous
Driving
• Universal Semantic Segmentation for Fisheye Urban Driving Images
• UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a
Generic Framework for Handling Common Camera Distortion Models
• OmniDet: Surround View Cameras based Multi-task Visual Perception
Network for Autonomous Driving
• Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/introduction-to-simultaneous-localization-and-mapping-slam-a-presentation-from-gareth-cross/
Independent game developer (and former technical lead of state estimation at Skydio) Gareth Cross presents the “Introduction to Simultaneous Localization and Mapping (SLAM)” tutorial at the May 2021 Embedded Vision Summit.
This talk provides an introduction to the fundamentals of simultaneous localization and mapping (SLAM). Cross aims to provide foundational knowledge, and viewers are not expected to have any prerequisite experience in the field.
The talk consists of an introduction to the concept of SLAM, as well as practical design considerations in formulating SLAM problems. Visual inertial odometry is introduced as a motivating example of SLAM, and Cross explains how this problem is structured and solved.
Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry...Masaya Kaneko
SfMLearner + KF selectionを提案した"Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM [ICCV19]"を論文読み会で紹介した時の資料です.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sep-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Raghuraman Krishnamoorthi, Software Engineer at Facebook, delivers the presentation "Quantizing Deep Networks for Efficient Inference at the Edge" at the Embedded Vision Alliance's September 2019 Vision Industry and Technology Forum. Krishnamoorthi gives an overview of practical deep neural network quantization techniques and tools.
Fisheye/Omnidirectional View in Autonomous Driving VYu Huang
Road-line detection and 3D reconstruction using fisheye cameras
• Vehicle Re-ID for Surround-view Camera System
• SynDistNet: Self-Supervised Monocular Fisheye Camera Distance
Estimation Synergized with Semantic Segmentation for Autonomous
Driving
• Universal Semantic Segmentation for Fisheye Urban Driving Images
• UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a
Generic Framework for Handling Common Camera Distortion Models
• OmniDet: Surround View Cameras based Multi-task Visual Perception
Network for Autonomous Driving
• Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving
Low-cost infrared camera arrays for enhanced capabilities such as Integral (light-field) imaging, Pixel super-resolution, and multi-spectral imaging. Talk presented by Dr. Miguel Preciado at the University of Glasgow, optical sciences seminar.
Link to download the presentation: https://docs.google.com/presentation/d/1wtWAsQmOzySzbIyBXhN7Pg1cDBv7D-bJqyV-2TCEKN8/edit?usp=sharing
Real-time Moving Object Detection using SURFiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
SkyStitch: a Cooperative Multi-UAV-based Real-time Video Surveillance System ...Kitsukawa Yuki
パターン・映像情報処理特論において論文を紹介した時の発表資料です。
Xiangyun Meng, Wei Wang, and Ben Leong. 2015. SkyStitch: A Cooperative Multi-UAV-based Real-time Video Surveillance System with Stitching. In Proceedings of the 23rd ACM international conference on Multimedia (MM '15). ACM, New York, NY, USA, 261-270. DOI=http://dx.doi.org/10.1145/2733373.2806225
Vehicle Recognition at Night Based on Tail LightDetection Using Image ProcessingIJRES Journal
Automatic recognition of vehicles in front can be used as a component of systems for forward collisions prevention. When driving in dark conditions, vehicles in front are generally visible by their back lights. Present an algorithm that detects vehicles at night using a camera by searching for tail lights. Develop an image processing systems that can efficiently spot vehicles at different distances and in weather and lightning conditions.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Similar to Fisheye/Omnidirectional View in Autonomous Driving IV (20)
Application of Foundation Model for Autonomous DrivingYu Huang
Since DARPA’s Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. Recently powered by large language models (LLMs), chat systems, such as chatGPT and PaLM, emerge and rapidly become a promising direction to achieve artificial general intelligence (AGI) in natural language processing (NLP). There comes a natural thinking that we could employ these abilities to reformulate autonomous driving. By combining LLM with foundation models, it is possible to utilize the human knowledge, commonsense and reasoning to rebuild autonomous driving systems from the current long-tailed AI dilemma. In this paper, we investigate the techniques of foundation models and LLMs applied for autonomous driving, categorized as simulation, world model, data annotation and planning or E2E solutions etc.
Autonomous driving for robotaxi, like perception, prediction, planning, decision making and control etc. As well as simulation, visualization and data closed loop etc.
LiDAR in the Adverse Weather: Dust, Snow, Rain and Fog (2)Yu Huang
Canadian Adverse Driving Conditions Dataset, 2020, 2
Deep multimodal sensor fusion in unseen adverse weather, 2020, 8
RADIATE: A Radar Dataset for Automotive Perception in Bad Weather, 2021, 4
Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection, 2021, 7
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather, 2021, 8
DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather, 2021, 9
Scenario-Based Development & Testing for Autonomous DrivingYu Huang
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World, 2020
A Scenario-Based Development Framework for Autonomous Driving, 2020
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving, 2020
Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction, 2021
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles, 2021
Systems Approach to Creating Test Scenarios for Automated Driving Systems, Reliability Engineering and System Safety (215), 2021
How to Build a Data Closed-loop Platform for Autonomous Driving?Yu Huang
Introduction;
data driven models for autonomous driving;
cloud computing infrastructure and big data processing;
annotation tools for training data;
large scale model training platform;
model testing and verification;
related machine learning techniques;
Conclusion.
Simulation for autonomous driving at uber atgYu Huang
Testing Safety of SDVs by Simulating Perception and Prediction
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Recovering and Simulating Pedestrians in the Wild
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
SceneGen: Learning to Generate Realistic Traffic Scenes
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Appendix: (Waymo)
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
RegNet: Multimodal Sensor Registration Using Deep Neural Networks
CalibNet: Self-Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
RGGNet: Tolerance Aware LiDAR-Camera Online Calibration with Geometric Deep Learning and Generative Model
CalibRCNN: Calibrating Camera and LiDAR by Recurrent Convolutional Neural Network and Geometric Constraints
LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network
CFNet: LiDAR-Camera Registration Using Calibration Flow Network
Prediction and planning for self driving at waymoYu Huang
ChauffeurNet: Learning To Drive By Imitating The Best Synthesizing The Worst
Multipath: Multiple Probabilistic Anchor Trajectory Hypotheses For Behavior Prediction
VectorNet: Encoding HD Maps And Agent Dynamics From Vectorized Representation
TNT: Target-driven Trajectory Prediction
Large Scale Interactive Motion Forecasting For Autonomous Driving : The Waymo Open Motion Dataset
Identifying Driver Interactions Via Conditional Behavior Prediction
Peeking Into The Future: Predicting Future Person Activities And Locations In Videos
STINet: Spatio-temporal-interactive Network For Pedestrian Detection And Trajectory Prediction
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
2. Outline
• FisheyeMultiNet: Real-time Multi-task Learning Architecture for
Surround-view Automated Parking System
• Generalized Object Detection on Fisheye Cameras for Autonomous
Driving: Dataset, Representations and Baseline
• SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for
Autonomous Driving
• Feasible Self-Calibration of Larger Field-of-View (FOV) Camera Sensors
for the ADAS
3. FisheyeMultiNet: Real-time Multi-task Learning Architecture
for Surround-view Automated Parking System
• Automated Parking is a low speed maneuvering scenario which is quite
unstructured and complex, requiring full 360° near-field sensing around the
vehicle.
• In this paper, discuss the design and implementation of an automated parking
system from the perspective of camera based deep learning algorithms.
• provide a holistic overview of an industrial system covering the embedded
system, use cases and the deep learning architecture.
• demonstrate a real-time multi-task deep learning network called
FisheyeMultiNet, which detects all the necessary objects for parking on a low-
power embedded system.
• FisheyeMultiNet runs at 15 fps for 4 cameras and it has three tasks namely object
detection, semantic segmentation and soiling detection.
• release a partial dataset of 5,000 images containing semantic segmentation and
bounding box detection ground truth via WoodScape project.
7. FisheyeMultiNet: Real-time Multi-task Learning Architecture
for Surround-view Automated Parking System
Illustration of FisheyeMultiNet architecture comprising of object detection, semantic segmentation and soiling detection tasks.
9. Generalized Object Detection on Fisheye Cameras for
Autonomous Driving: Dataset, Representations and Baseline
• Object detection is a comprehensively studied problem in autonomous driving.
• However, it has been relatively less explored in the case of fisheye cameras.
• The standard bounding box fails in fisheye cameras due to the strong radial distortion,
particularly in the image’s periphery.
• explore better representations like oriented bounding box, ellipse, and generic polygon
for object detection in fisheye images in this work.
• use the IoU metric to compare these representations using accurate instance
segmentation ground truth.
• design a novel curved bounding box model that has optimal properties for fisheye
distortion models.
• also design a curvature adaptive perimeter sampling method for obtaining polygon
vertices, improving relative mAP score by 4.9% compared to uniform sampling.
• Overall, the proposed polygon model improves mIoU relative accuracy by 40.3%.
• The dataset comprising of 10,000 images along with ground truth will be made public.
10. Generalized Object Detection on Fisheye Cameras for
Autonomous Driving: Dataset, Representations and Baseline
Left: Illustration of fisheye distortion of projection of an open cube. A 4th-degree polynomial model radial
distortion. can visually notice that box matures to a curved box. Right: propose the Curved Bounding Box
using a circle with an arbitrary center and radius, as illustrated. It captures the radial distortion and obtains a
better footpoint. The center of the circle can be equivalently reparameterized using the object center (xˆ, yˆ).
12. Generalized Object Detection on Fisheye Cameras for
Autonomous Driving: Dataset, Representations and Baseline
Generic Polygon Representations. Left: Uniform angular sampling where the intersection of the polygon with the
radial line is represented by one parameter per point (r). Middle: Uniform contour sampling using L2 distance. It can
be parameterized in polar co-ordinates using 3 parameters (r, θ, α). α denotes the number of polygon vertices within
the sector, and it may be used to simplify the training. Alternatively, 2 parameters (x,y) can be used, as shown in the
figure on the right. Right: Variable step contour sampling. It is shown that the straight line in the bottom has less
number of points than curved points such as the wheel. This representation allows to maximize the utilization of
vertices according to local curvature.
13. Generalized Object Detection on Fisheye Cameras for
Autonomous Driving: Dataset, Representations and Baseline
FisheyeYOLO is an extension of YOLOv3 which
can output different output representation
15. SynWoodScape: Synthetic Surround-view Fisheye
Camera Dataset for Autonomous Driving
• Four fisheye cameras with a 190° field of view cover the 360° around the vehicle.
• Due to its high radial distortion, the standard algorithms do not extend easily.
• In this work, release a synthetic version of the surround-view dataset, covering many of its
weaknesses and extending it.
• Firstly, it is not possible to obtain ground truth for pixel-wise optical flow and depth.
• Secondly, WoodScape did not have all four cameras simultaneously in order to sample diverse
frames.
• However, this means that multi-camera algorithms cannot be designed, which is enabled in the
new dataset.
• implemented surround-view fisheye geometric projections in CARLA Simulator matching
WoodScape’s configuration and created SynWoodScape.
• release 80k images with annotations for 10+ tasks.
• also release the baseline code and supporting scripts.
21. SynWoodScape: Synthetic Surround-view Fisheye
Camera Dataset for Autonomous Driving
SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving
22. SynWoodScape: Synthetic Surround-view Fisheye
Camera Dataset for Autonomous Driving
Overview of Surround View cameras based multi-task
visual perception framework. The distance estimation task
(blue block) makes use of semantic guidance and dynamic
object masking from semantic/motion estimation (green
and blue haze block) and camera-geometry adaptive
convolutions (orange block). Additionally, guide the
detection decoder features (gray block) with the semantic
features. The encoder block (shown in the same color) is
common for all the tasks. The framework consists of
processing blocks to train the self-supervised distance
estimation (blue blocks) and semantic segmentation
(green blocks), motion segmentation (blue haze blocks),
and polygon-based fisheye object detection (gray blocks).
obtain Surround View geometric information by post-
processing the predicted distance maps in 3D space
(perano block). The camera tensor Ct (orange block) helps
OmniDet yield distance maps on multiple camera-
viewpoints and make the network camera independent.
25. Feasible Self-Calibration of Larger Field-of-
View (FOV) Camera Sensors for ADAS
• This paper proposes a self-calibration method that can be applied for multiple larger
field-of-view (FOV) camera models on ADAS.
• Firstly, perform steps such as edge detection, length thresholding, and edge grouping for
the segregation of robust line candidates from the pool of initial distortion line segments.
• A straightness cost constraint with a cross-entropy loss was imposed on the selected line
candidates, thereby exploiting that loss to optimize the lens-distortion parameters using
the Levenberg–Marquardt (LM) optimization approach.
• The best-fit distortion parameters are used for the undistortion of an image frame,
thereby employing various high-end vision-based tasks on the distortion-rectified frame.
• investigation on experimental approaches such as parameter sharing between multiple
camera systems and model-specific empirical γ-residual rectification factor.
• The quantitative comparisons between the proposed method and traditional OpenCV
method on KITTI dataset with synthetically generated distortion ranges.
• a pragmatic approach of qualitative analysis has been conducted through streamlining
high-end vision-based tasks such as object detection, localization, and mapping, and
auto-parking on undistorted frames.
26. Feasible Self-Calibration of Larger Field-of-
View (FOV) Camera Sensors for ADAS
Proposed Pipeline on ADAS workbench (a) ADAS Platform: Camera sensors setup and image acquisition
27. Feasible Self-Calibration of Larger Field-of-
View (FOV) Camera Sensors for ADAS
Proposed Pipeline on ADAS workbench (b) Proposed method with block schematics.
28. Feasible Self-Calibration of Larger Field-of-
View (FOV) Camera Sensors for ADAS
Structural anomaly induced into a
scene due to heavy lens distortion
caused by wide-angle cameras with
field-of-view 120◦ < FOV < 140◦.
29. Feasible Self-Calibration of Larger Field-of-
View (FOV) Camera Sensors for ADAS
Lens Projection Models: (a) Standard Camera Pinhole Projection Model. (b) Larger FOV Lens Orthogonal Projection Model.
30. Feasible Self-Calibration of Larger Field-of-
View (FOV) Camera Sensors for ADAS
Proposed Self-calibration design
31. Feasible Self-Calibration of Larger Field-of-
View (FOV) Camera Sensors for ADAS
Pre-processing of line candidates and
Estimation of Straightness constraint.
32. Feasible Self-Calibration of Larger Field-of-
View (FOV) Camera Sensors for ADAS
Schematic of distortion parameter
estimation using LM-optimization in normal
mode and parameter sharing mode.
37. Feasible Self-Calibration of Larger Field-of-
View (FOV) Camera Sensors for ADAS
Severe distortion cases rectified
using several approaches
[28,29], proposed method with
and without empirical γ-hyper
parameter.
50. Feasible Self-Calibration of Larger Field-of-
View (FOV) Camera Sensors for ADAS
Auto-parking scenario on rear fisheye camera: Real-time visual SLAM pipeline on lens distortion rectified sensor data.